{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Fully Connected Neural Network without FFT**\n",
    "- input shape: 160\n",
    "- library: tensorflow\n",
    "- Training dataset: Basic and CDD-based (comparison)\n",
    "- Test dataset: Basic, CDD-based and External\n",
    "- Metric: Accuracy, F1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\h5py\\__init__.py:36: UserWarning: h5py is running against HDF5 1.14.6 when it was built against 1.14.5, this may cause problems\n",
      "  _warn((\"h5py is running against HDF5 {0} when it was built against {1}, \"\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "cannot import name '_is_pandas_dataframe' from 'matplotlib.cbook' (c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Dense, ReLU, BatchNormalization,  Dropout\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Sequential, load_model\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m shuffle\n",
      "File \u001b[1;32mc:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\pyplot.py:70\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _docstring\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     69\u001b[0m     FigureCanvasBase, FigureManagerBase, MouseButton)\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfigure\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Figure, FigureBase, figaspect\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgridspec\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m GridSpec, SubplotSpec\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m rcsetup, rcParamsDefault, rcParamsOrig\n",
      "File \u001b[1;32mc:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\figure.py:40\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[1;32m---> 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _blocking_input, backend_bases, _docstring, projections\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01martist\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     42\u001b[0m     Artist, allow_rasterization, _finalize_rasterization)\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     44\u001b[0m     DrawEvent, FigureCanvasBase, NonGuiException, MouseButton, _get_renderer)\n",
      "File \u001b[1;32mc:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\projections\\__init__.py:55\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mNon-separable transforms that map from data space to screen space.\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;124;03m`matplotlib.projections.polar` may also be of interest.\u001b[39;00m\n\u001b[0;32m     53\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 55\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m axes, _docstring\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeo\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m AitoffAxes, HammerAxes, LambertAxes, MollweideAxes\n\u001b[0;32m     57\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolar\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PolarAxes\n",
      "File \u001b[1;32mc:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\axes\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _base\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_axes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Axes\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Backcompat.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\axes\\_base.py:27\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrcsetup\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m cycler, validate_axisbelow\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspines\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmspines\u001b[39;00m\n\u001b[1;32m---> 27\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtable\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmtable\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtext\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmtext\u001b[39;00m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mticker\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmticker\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\table.py:36\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Bbox\n\u001b[0;32m     34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpath\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m---> 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcbook\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _is_pandas_dataframe\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mCell\u001b[39;00m(Rectangle):\n\u001b[0;32m     40\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;124;03m    A cell is a `.Rectangle` with some associated `.Text`.\u001b[39;00m\n\u001b[0;32m     42\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;124;03m    `.Table.add_cell`.\u001b[39;00m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name '_is_pandas_dataframe' from 'matplotlib.cbook' (c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py)"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn import metrics\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.models import load_model\n",
    "import keras\n",
    "from keras.layers import Dense, ReLU, BatchNormalization,  Dropout\n",
    "from keras.models import Sequential, load_model\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.utils import shuffle\n",
    "import time\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Definition of reusable functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eliminate_transient(df):\n",
    "    no_transient = df[(df[0] == 1) | (df[0] == -1)]\n",
    "    no_transient.loc[no_transient[0] == -1, 0] = 0\n",
    "    num_pos = no_transient[0].value_counts()[1]\n",
    "    num_neg = no_transient[0].value_counts()[0]\n",
    "    return no_transient.to_numpy(), num_pos, num_neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "########## Dealing with Nan #############################\n",
    "# know bug, cannot deal with nan in consecutive np.nan value\n",
    "def replaceNan(original_array):\n",
    "    an_np_array = np.copy(original_array)\n",
    "    if not np.isnan(an_np_array).any():\n",
    "        return an_np_array\n",
    "    else:\n",
    "        for index in np.argwhere(np.isnan(an_np_array)):\n",
    "            if index[1] == 0:\n",
    "                an_np_array[index[0],index[1]] = an_np_array[index[0],index[1]+1]\n",
    "            else:\n",
    "                an_np_array[index[0],index[1]] = an_np_array[index[0],index[1]-1]\n",
    "    return an_np_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Plot the data\n",
    "def linearlize_and_plot(df):\n",
    "    data_one_raw = []\n",
    "    for row in df:\n",
    "        data_one_raw = np.append(data_one_raw,row)\n",
    "    return data_one_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### expand label with certain replication, in arc detection case, the ratio is 160\n",
    "def label_expansion(df,expansion_ratio):\n",
    "    label_expansion = []\n",
    "    for row in df:\n",
    "        label_expansion = np.append(label_expansion,np.full(expansion_ratio,row))\n",
    "    return label_expansion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_and_label(data, label, save_figure = False, filename=\"\", title=\"\"):\n",
    "    fig, ax1 = plt.subplots()\n",
    "    color = 'tab:red'\n",
    "    ax1.set_xlabel('datapoint')\n",
    "    ax1.set_ylabel('current', color='blue')\n",
    "    ax1.plot(linearlize_and_plot(data), color='blue')\n",
    "    ax1.plot(label_expansion(label/100,160), color='red')\n",
    "    ax1.tick_params(axis='y', labelcolor='blue')\n",
    "    ax1.set_title(title)\n",
    "    if save_figure:\n",
    "        fig.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  1. nan  2.]\n",
      " [ 0. nan nan  5.]]\n",
      "[[0. 1. 1. 2.]\n",
      " [0. 0. 0. 5.]]\n"
     ]
    }
   ],
   "source": [
    "array = np.array([[0,1,np.nan,2],[0,np.nan,np.nan,5]])\n",
    "replaced = replaceNan(array)\n",
    "print(array)\n",
    "print(replaced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dataframe to numpy array, split the dataset into data and label, generate train, valid and test dataset at the same time\n",
    "def shuffle_normalise_split_dataset(original_array, train_ratio, valid_ratio, test_ratio,shuffle_data=True):\n",
    "    np_array = replaceNan(original_array)\n",
    "    np_data = np.copy(np_array[:,1:])\n",
    "    np_label = np.copy(np_array[:,0:1])\n",
    "    zero_no_current_status(np_data,0.005)\n",
    "    np_data_normalised = normalize(np_data, axis=1, norm='l1')\n",
    "    np_normalized = np.concatenate((np_label,np_data_normalised),axis=1)\n",
    "    if shuffle:\n",
    "        np_shuffle = shuffle(np_normalized)\n",
    "    else:\n",
    "        np_shuffle = np_normalized\n",
    "    train_index = int(len(np_shuffle) * train_ratio)       #length of the train data\n",
    "    trainset = np_shuffle[0:train_index]                      \n",
    "    valid_index = int(len(np_shuffle) * valid_ratio)                         #length of the valid_set\n",
    "    validset = np_shuffle[train_index : train_index+valid_index]            #the valid_dataframe from the pandas dataframe\n",
    "    testset = np_shuffle[train_index+valid_index : ]\n",
    "\n",
    "    return  np_data_normalised, np_label, trainset[:,1:], trainset[:,0], validset[:,1:], validset[:,0], testset[:,1:], testset[:,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4 5 6]\n",
      " [0 7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "# Create two sample NumPy arrays\n",
    "array1 = np.array([[1],[0]])\n",
    "array2 = np.array([[4, 5, 6],[7,8,9]])\n",
    "\n",
    "# Concatenate the arrays along the specified axis (0 for rows, 1 for columns)\n",
    "concatenated_array = np.concatenate((array1, array2),axis=1)\n",
    "\n",
    "print(concatenated_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_no_current_status(np_array, threshold):\n",
    "    np_array[np_array<threshold] = threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.332 0.332]\n",
      " [0.001 0.001]]\n"
     ]
    }
   ],
   "source": [
    "array = np.array([[0.332,0.332],[0.0001,0.0001]])\n",
    "zero_no_current_status(array,0.001)\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def result_log (model=\"model type\", test=\"test dataset\", filename = \"evaluation_log.txt\", cf_matrix = None, accuracy_score = None, f1_score = None):\n",
    "    ts = time.time()\n",
    "    sttime = datetime.datetime.fromtimestamp(ts).strftime('%Y%m%d_%H:%M:%S - ')\n",
    "    with open(filename,'a') as log_file:\n",
    "        log_file.write(sttime +'\\n')\n",
    "        log_file.write(\"Result of \" + model + \" on \" + test +'\\n')\n",
    "        log_file.write(f\"Accuracy: {accuracy_score:.4f}\" + '\\n')\n",
    "        log_file.write(f\"F1 score: {f1_score:.4f}\" + '\\n')\n",
    "        for line in cf_matrix:\n",
    "            log_file.write(\" \".join([str(n) for n in line]) + \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluation(model,test_set,test_label,test_set_name,filename,model_name):\n",
    "    predict_probability = model.predict(\n",
    "        test_set,\n",
    "        batch_size = 64\n",
    "    )\n",
    "    predict_label = np.where(predict_probability > 0.5, 1, 0)\n",
    "    cf_matrix = metrics.confusion_matrix(test_label, predict_label)\n",
    "    accuracy_score = metrics.accuracy_score(test_label, predict_label)\n",
    "    f1score = metrics.f1_score(test_label, predict_label)\n",
    "    # save results\n",
    "    \n",
    "    result_log(model=model_name, test=test_set_name, cf_matrix=cf_matrix, accuracy_score = accuracy_score,f1_score = f1score,filename=filename)\n",
    "    # save plot as proof of training procedure so that no overfitting \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_the_train_history(train_val_history, filename):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(train_val_history.history['binary_accuracy'])\n",
    "    ax.plot(train_val_history.history['val_binary_accuracy'])\n",
    "    ax.set_title('model accuracy')\n",
    "    ax.set_ylabel('accuracy')\n",
    "    ax.set_xlabel('epoch')\n",
    "    ax.legend(['train', 'val'], loc='upper left')\n",
    "    fig.savefig(filename)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting Point of the program"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data and preprocess\n",
    "1. delete transient part\n",
    "2. shuffle dastaset and split them into train, valid and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "M_df = pd.read_csv(\"../data/Basic_dataset.csv\", header=None)\n",
    "MV_df = pd.read_csv(\"../data/CDD_based_dataset.csv\", header=None)\n",
    "MF_df = pd.read_csv(\"../data/External_dataset.csv\", header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#----------------------------------------------------model------------------------------------------------------\n",
    "def fnn_training_no_fft(train_set, train_label, valid_set, valid_label, epoch):\n",
    "\n",
    "    epochs = epoch\n",
    "    batch_size = 64\n",
    "    dropout = 0.25\n",
    "    l2 = tf.keras.regularizers.L2(l2=1e-5)\n",
    "    num_pos = len(train_label[train_label==1])\n",
    "    num_neg = len(train_label[train_label==0])\n",
    "    total_samples = num_pos + num_neg\n",
    "    output_bias = np.log([num_pos / num_neg])                 # for the output layer (imbalance data)\n",
    "    weight_class_0 = (1 / num_neg) * (total_samples / 2.0)    # weights for the loss function (imbalance data)\n",
    "    weight_class_1 = (1 / num_pos) * (total_samples / 2.0)\n",
    "    class_weights = {0: weight_class_0,\n",
    "                    1: weight_class_1}\n",
    "    initializer = tf.keras.initializers.GlorotNormal()\n",
    "\n",
    "    ################################# architecture\n",
    "    model = Sequential([\n",
    "        Dense(320, kernel_regularizer=l2, use_bias=True, kernel_initializer=initializer,input_shape=(160,)),\n",
    "        BatchNormalization(),\n",
    "        ReLU(),\n",
    "        Dropout(dropout),\n",
    "\n",
    "        Dense(640, kernel_regularizer=l2, use_bias=True, kernel_initializer=initializer),\n",
    "        BatchNormalization(),\n",
    "        ReLU(),\n",
    "        Dropout(dropout),\n",
    "\n",
    "        Dense(480, kernel_regularizer=l2, use_bias=True, kernel_initializer=initializer),\n",
    "        BatchNormalization(),\n",
    "        ReLU(),\n",
    "        Dropout(dropout),\n",
    "\n",
    "        Dense(1, activation='sigmoid'),\n",
    "    ])\n",
    "\n",
    "    #############################  compiling\n",
    "    metric_accuracy = keras.metrics.BinaryAccuracy()\n",
    "    metric_f1score = tf.keras.metrics.F1Score(threshold=0.5)\n",
    "    model.compile(\n",
    "        optimizer= Adam(learning_rate= 1e-6),\n",
    "        loss= keras.losses.BinaryCrossentropy(),\n",
    "        metrics= [metric_accuracy]\n",
    "    )\n",
    "\n",
    "    ############################## training\n",
    "    train_val_history = model.fit(\n",
    "        train_set,\n",
    "        train_label,\n",
    "        batch_size=batch_size,\n",
    "        epochs=epochs,\n",
    "        validation_data=(valid_set, valid_label),\n",
    "        shuffle=True,\n",
    "        class_weight=class_weights\n",
    "    )\n",
    "    return model, train_val_history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 24ms/step - loss: 1.0172 - binary_accuracy: 0.8447 - val_loss: 0.6892 - val_binary_accuracy: 0.7149\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.7315 - binary_accuracy: 0.8911 - val_loss: 0.6277 - val_binary_accuracy: 0.7448\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.5766 - binary_accuracy: 0.9190 - val_loss: 0.5270 - val_binary_accuracy: 0.8735\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.4691 - binary_accuracy: 0.9386 - val_loss: 0.4913 - val_binary_accuracy: 0.8968\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4089 - binary_accuracy: 0.9512 - val_loss: 0.4751 - val_binary_accuracy: 0.8951\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3732 - binary_accuracy: 0.9561 - val_loss: 0.4373 - val_binary_accuracy: 0.9094\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3369 - binary_accuracy: 0.9639 - val_loss: 0.4267 - val_binary_accuracy: 0.9098\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 4s 15ms/step - loss: 0.3140 - binary_accuracy: 0.9649 - val_loss: 0.4264 - val_binary_accuracy: 0.9100\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2849 - binary_accuracy: 0.9699 - val_loss: 0.3899 - val_binary_accuracy: 0.9123\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2764 - binary_accuracy: 0.9703 - val_loss: 0.3904 - val_binary_accuracy: 0.9121\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2664 - binary_accuracy: 0.9701 - val_loss: 0.3924 - val_binary_accuracy: 0.9122\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2495 - binary_accuracy: 0.9718 - val_loss: 0.3701 - val_binary_accuracy: 0.9132\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2405 - binary_accuracy: 0.9726 - val_loss: 0.3846 - val_binary_accuracy: 0.9146\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2331 - binary_accuracy: 0.9717 - val_loss: 0.3747 - val_binary_accuracy: 0.9130\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2212 - binary_accuracy: 0.9734 - val_loss: 0.3624 - val_binary_accuracy: 0.9117\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2146 - binary_accuracy: 0.9734 - val_loss: 0.3485 - val_binary_accuracy: 0.9132\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2096 - binary_accuracy: 0.9739 - val_loss: 0.3479 - val_binary_accuracy: 0.9125\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2036 - binary_accuracy: 0.9736 - val_loss: 0.3450 - val_binary_accuracy: 0.9144\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.1937 - binary_accuracy: 0.9755 - val_loss: 0.3374 - val_binary_accuracy: 0.9126\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1940 - binary_accuracy: 0.9741 - val_loss: 0.3463 - val_binary_accuracy: 0.9133\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1846 - binary_accuracy: 0.9750 - val_loss: 0.3359 - val_binary_accuracy: 0.9129\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.1789 - binary_accuracy: 0.9745 - val_loss: 0.3283 - val_binary_accuracy: 0.9130\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1781 - binary_accuracy: 0.9742 - val_loss: 0.3350 - val_binary_accuracy: 0.9126\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1665 - binary_accuracy: 0.9773 - val_loss: 0.3254 - val_binary_accuracy: 0.9121\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1665 - binary_accuracy: 0.9761 - val_loss: 0.3247 - val_binary_accuracy: 0.9129\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1687 - binary_accuracy: 0.9754 - val_loss: 0.3250 - val_binary_accuracy: 0.9138\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1668 - binary_accuracy: 0.9741 - val_loss: 0.3257 - val_binary_accuracy: 0.9140\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1597 - binary_accuracy: 0.9758 - val_loss: 0.3172 - val_binary_accuracy: 0.9129\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1559 - binary_accuracy: 0.9769 - val_loss: 0.3143 - val_binary_accuracy: 0.9137\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1545 - binary_accuracy: 0.9767 - val_loss: 0.3184 - val_binary_accuracy: 0.9148\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1505 - binary_accuracy: 0.9772 - val_loss: 0.3168 - val_binary_accuracy: 0.9144\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1556 - binary_accuracy: 0.9762 - val_loss: 0.3338 - val_binary_accuracy: 0.9123\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1544 - binary_accuracy: 0.9748 - val_loss: 0.3296 - val_binary_accuracy: 0.9126\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1494 - binary_accuracy: 0.9762 - val_loss: 0.3250 - val_binary_accuracy: 0.9118\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1425 - binary_accuracy: 0.9761 - val_loss: 0.3209 - val_binary_accuracy: 0.9106\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 26ms/step - loss: 0.6703 - binary_accuracy: 0.6179 - val_loss: 0.7112 - val_binary_accuracy: 0.1867\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.5439 - binary_accuracy: 0.8181 - val_loss: 0.6331 - val_binary_accuracy: 0.9124\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 5s 17ms/step - loss: 0.4693 - binary_accuracy: 0.8658 - val_loss: 0.4649 - val_binary_accuracy: 0.9498\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.4222 - binary_accuracy: 0.8823 - val_loss: 0.3975 - val_binary_accuracy: 0.9490\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.3864 - binary_accuracy: 0.8919 - val_loss: 0.3703 - val_binary_accuracy: 0.9515\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 4s 14ms/step - loss: 0.3606 - binary_accuracy: 0.8975 - val_loss: 0.3393 - val_binary_accuracy: 0.9512\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3407 - binary_accuracy: 0.9023 - val_loss: 0.3089 - val_binary_accuracy: 0.9519\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3240 - binary_accuracy: 0.9036 - val_loss: 0.2969 - val_binary_accuracy: 0.9512\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3138 - binary_accuracy: 0.9077 - val_loss: 0.2735 - val_binary_accuracy: 0.9513\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3037 - binary_accuracy: 0.9068 - val_loss: 0.2632 - val_binary_accuracy: 0.9516\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2945 - binary_accuracy: 0.9091 - val_loss: 0.2510 - val_binary_accuracy: 0.9517\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2858 - binary_accuracy: 0.9121 - val_loss: 0.2432 - val_binary_accuracy: 0.9522\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2825 - binary_accuracy: 0.9131 - val_loss: 0.2383 - val_binary_accuracy: 0.9517\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2744 - binary_accuracy: 0.9141 - val_loss: 0.2326 - val_binary_accuracy: 0.9523\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2712 - binary_accuracy: 0.9116 - val_loss: 0.2317 - val_binary_accuracy: 0.9523\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2655 - binary_accuracy: 0.9130 - val_loss: 0.2300 - val_binary_accuracy: 0.9519\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2607 - binary_accuracy: 0.9143 - val_loss: 0.2217 - val_binary_accuracy: 0.9522\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2584 - binary_accuracy: 0.9161 - val_loss: 0.2197 - val_binary_accuracy: 0.9532\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2534 - binary_accuracy: 0.9195 - val_loss: 0.2165 - val_binary_accuracy: 0.9527\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2513 - binary_accuracy: 0.9175 - val_loss: 0.2127 - val_binary_accuracy: 0.9532\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2499 - binary_accuracy: 0.9163 - val_loss: 0.2142 - val_binary_accuracy: 0.9527\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2451 - binary_accuracy: 0.9159 - val_loss: 0.2078 - val_binary_accuracy: 0.9532\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2489 - binary_accuracy: 0.9156 - val_loss: 0.2077 - val_binary_accuracy: 0.9539\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2413 - binary_accuracy: 0.9192 - val_loss: 0.2055 - val_binary_accuracy: 0.9534\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2406 - binary_accuracy: 0.9165 - val_loss: 0.2064 - val_binary_accuracy: 0.9537\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2371 - binary_accuracy: 0.9196 - val_loss: 0.2055 - val_binary_accuracy: 0.9537\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2382 - binary_accuracy: 0.9164 - val_loss: 0.2021 - val_binary_accuracy: 0.9542\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2364 - binary_accuracy: 0.9161 - val_loss: 0.2012 - val_binary_accuracy: 0.9547\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2333 - binary_accuracy: 0.9169 - val_loss: 0.1979 - val_binary_accuracy: 0.9544\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2325 - binary_accuracy: 0.9177 - val_loss: 0.1976 - val_binary_accuracy: 0.9547\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2317 - binary_accuracy: 0.9196 - val_loss: 0.1982 - val_binary_accuracy: 0.9544\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2291 - binary_accuracy: 0.9179 - val_loss: 0.1975 - val_binary_accuracy: 0.9546\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2309 - binary_accuracy: 0.9188 - val_loss: 0.1953 - val_binary_accuracy: 0.9543\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2324 - binary_accuracy: 0.9137 - val_loss: 0.1954 - val_binary_accuracy: 0.9549\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2323 - binary_accuracy: 0.9144 - val_loss: 0.1947 - val_binary_accuracy: 0.9556\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n",
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "279/279 [==============================] - 5s 11ms/step - loss: 0.6605 - binary_accuracy: 0.7303 - val_loss: 0.7016 - val_binary_accuracy: 0.7242\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.5287 - binary_accuracy: 0.9036 - val_loss: 0.6391 - val_binary_accuracy: 0.8122\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.4423 - binary_accuracy: 0.9528 - val_loss: 0.5301 - val_binary_accuracy: 0.9056\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4007 - binary_accuracy: 0.9615 - val_loss: 0.4856 - val_binary_accuracy: 0.8957\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3563 - binary_accuracy: 0.9689 - val_loss: 0.4695 - val_binary_accuracy: 0.8983\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3253 - binary_accuracy: 0.9734 - val_loss: 0.4434 - val_binary_accuracy: 0.9079\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3053 - binary_accuracy: 0.9722 - val_loss: 0.4267 - val_binary_accuracy: 0.9098\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2821 - binary_accuracy: 0.9737 - val_loss: 0.4016 - val_binary_accuracy: 0.9091\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2633 - binary_accuracy: 0.9749 - val_loss: 0.4070 - val_binary_accuracy: 0.9113\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2531 - binary_accuracy: 0.9743 - val_loss: 0.3731 - val_binary_accuracy: 0.9123\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2365 - binary_accuracy: 0.9754 - val_loss: 0.3784 - val_binary_accuracy: 0.9152\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2234 - binary_accuracy: 0.9770 - val_loss: 0.3595 - val_binary_accuracy: 0.9130\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2147 - binary_accuracy: 0.9765 - val_loss: 0.3566 - val_binary_accuracy: 0.9144\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2067 - binary_accuracy: 0.9770 - val_loss: 0.3477 - val_binary_accuracy: 0.9149\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1950 - binary_accuracy: 0.9774 - val_loss: 0.3476 - val_binary_accuracy: 0.9165\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1929 - binary_accuracy: 0.9762 - val_loss: 0.3471 - val_binary_accuracy: 0.9176\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1827 - binary_accuracy: 0.9778 - val_loss: 0.3414 - val_binary_accuracy: 0.9151\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1846 - binary_accuracy: 0.9762 - val_loss: 0.3480 - val_binary_accuracy: 0.9145\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1738 - binary_accuracy: 0.9763 - val_loss: 0.3384 - val_binary_accuracy: 0.9145\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1700 - binary_accuracy: 0.9781 - val_loss: 0.3356 - val_binary_accuracy: 0.9151\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1662 - binary_accuracy: 0.9776 - val_loss: 0.3351 - val_binary_accuracy: 0.9137\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1625 - binary_accuracy: 0.9769 - val_loss: 0.3334 - val_binary_accuracy: 0.9141\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1594 - binary_accuracy: 0.9772 - val_loss: 0.3118 - val_binary_accuracy: 0.9182\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1564 - binary_accuracy: 0.9770 - val_loss: 0.3118 - val_binary_accuracy: 0.9180\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1553 - binary_accuracy: 0.9768 - val_loss: 0.3347 - val_binary_accuracy: 0.9127\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1505 - binary_accuracy: 0.9776 - val_loss: 0.3314 - val_binary_accuracy: 0.9117\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1467 - binary_accuracy: 0.9772 - val_loss: 0.3212 - val_binary_accuracy: 0.9144\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1489 - binary_accuracy: 0.9758 - val_loss: 0.3299 - val_binary_accuracy: 0.9151\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1436 - binary_accuracy: 0.9765 - val_loss: 0.3217 - val_binary_accuracy: 0.9146\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1452 - binary_accuracy: 0.9762 - val_loss: 0.3214 - val_binary_accuracy: 0.9142\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1385 - binary_accuracy: 0.9771 - val_loss: 0.3221 - val_binary_accuracy: 0.9126\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 4s 13ms/step - loss: 0.1379 - binary_accuracy: 0.9782 - val_loss: 0.3208 - val_binary_accuracy: 0.9134\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.1400 - binary_accuracy: 0.9765 - val_loss: 0.3300 - val_binary_accuracy: 0.9077\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1321 - binary_accuracy: 0.9777 - val_loss: 0.3089 - val_binary_accuracy: 0.9171\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.1355 - binary_accuracy: 0.9783 - val_loss: 0.3296 - val_binary_accuracy: 0.9062\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 5ms/step\n",
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "279/279 [==============================] - 12s 23ms/step - loss: 0.5484 - binary_accuracy: 0.7527 - val_loss: 0.6831 - val_binary_accuracy: 0.8122\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.4613 - binary_accuracy: 0.8637 - val_loss: 0.5636 - val_binary_accuracy: 0.8735\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4135 - binary_accuracy: 0.8809 - val_loss: 0.3955 - val_binary_accuracy: 0.9556\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3734 - binary_accuracy: 0.8969 - val_loss: 0.3508 - val_binary_accuracy: 0.9578\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3457 - binary_accuracy: 0.9041 - val_loss: 0.2897 - val_binary_accuracy: 0.9594\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3289 - binary_accuracy: 0.9041 - val_loss: 0.2695 - val_binary_accuracy: 0.9600\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3140 - binary_accuracy: 0.9082 - val_loss: 0.2427 - val_binary_accuracy: 0.9591\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.3027 - binary_accuracy: 0.9116 - val_loss: 0.2331 - val_binary_accuracy: 0.9581\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2923 - binary_accuracy: 0.9124 - val_loss: 0.2251 - val_binary_accuracy: 0.9578\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2867 - binary_accuracy: 0.9126 - val_loss: 0.2184 - val_binary_accuracy: 0.9587\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2814 - binary_accuracy: 0.9143 - val_loss: 0.2109 - val_binary_accuracy: 0.9594\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2758 - binary_accuracy: 0.9134 - val_loss: 0.2077 - val_binary_accuracy: 0.9595\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2709 - binary_accuracy: 0.9164 - val_loss: 0.1930 - val_binary_accuracy: 0.9578\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2653 - binary_accuracy: 0.9148 - val_loss: 0.1890 - val_binary_accuracy: 0.9576\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2645 - binary_accuracy: 0.9156 - val_loss: 0.1935 - val_binary_accuracy: 0.9593\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2586 - binary_accuracy: 0.9174 - val_loss: 0.1864 - val_binary_accuracy: 0.9597\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2545 - binary_accuracy: 0.9177 - val_loss: 0.1847 - val_binary_accuracy: 0.9597\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2524 - binary_accuracy: 0.9175 - val_loss: 0.1840 - val_binary_accuracy: 0.9597\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2488 - binary_accuracy: 0.9187 - val_loss: 0.1822 - val_binary_accuracy: 0.9594\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2478 - binary_accuracy: 0.9196 - val_loss: 0.1809 - val_binary_accuracy: 0.9607\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2480 - binary_accuracy: 0.9181 - val_loss: 0.1772 - val_binary_accuracy: 0.9593\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2464 - binary_accuracy: 0.9171 - val_loss: 0.1758 - val_binary_accuracy: 0.9595\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2432 - binary_accuracy: 0.9202 - val_loss: 0.1757 - val_binary_accuracy: 0.9608\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2428 - binary_accuracy: 0.9174 - val_loss: 0.1732 - val_binary_accuracy: 0.9591\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.2396 - binary_accuracy: 0.9206 - val_loss: 0.1727 - val_binary_accuracy: 0.9607\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2421 - binary_accuracy: 0.9170 - val_loss: 0.1696 - val_binary_accuracy: 0.9595\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2361 - binary_accuracy: 0.9198 - val_loss: 0.1685 - val_binary_accuracy: 0.9603\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 4s 16ms/step - loss: 0.2381 - binary_accuracy: 0.9171 - val_loss: 0.1701 - val_binary_accuracy: 0.9604\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2316 - binary_accuracy: 0.9201 - val_loss: 0.1676 - val_binary_accuracy: 0.9597\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2319 - binary_accuracy: 0.9177 - val_loss: 0.1675 - val_binary_accuracy: 0.9605\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.2326 - binary_accuracy: 0.9211 - val_loss: 0.1662 - val_binary_accuracy: 0.9601\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 5s 17ms/step - loss: 0.2295 - binary_accuracy: 0.9202 - val_loss: 0.1641 - val_binary_accuracy: 0.9601\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2302 - binary_accuracy: 0.9189 - val_loss: 0.1633 - val_binary_accuracy: 0.9601\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2262 - binary_accuracy: 0.9215 - val_loss: 0.1645 - val_binary_accuracy: 0.9601\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2279 - binary_accuracy: 0.9202 - val_loss: 0.1648 - val_binary_accuracy: 0.9610\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 24ms/step - loss: 0.8961 - binary_accuracy: 0.8666 - val_loss: 0.6859 - val_binary_accuracy: 0.7285\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.6524 - binary_accuracy: 0.9043 - val_loss: 0.6177 - val_binary_accuracy: 0.7690\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.5165 - binary_accuracy: 0.9294 - val_loss: 0.5154 - val_binary_accuracy: 0.8859\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.4320 - binary_accuracy: 0.9464 - val_loss: 0.4854 - val_binary_accuracy: 0.8950\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3806 - binary_accuracy: 0.9555 - val_loss: 0.4419 - val_binary_accuracy: 0.9014\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3378 - binary_accuracy: 0.9644 - val_loss: 0.4155 - val_binary_accuracy: 0.9037\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3153 - binary_accuracy: 0.9677 - val_loss: 0.4253 - val_binary_accuracy: 0.9056\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2944 - binary_accuracy: 0.9686 - val_loss: 0.3878 - val_binary_accuracy: 0.9077\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2724 - binary_accuracy: 0.9711 - val_loss: 0.3768 - val_binary_accuracy: 0.9072\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2604 - binary_accuracy: 0.9710 - val_loss: 0.3756 - val_binary_accuracy: 0.9076\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2481 - binary_accuracy: 0.9710 - val_loss: 0.3735 - val_binary_accuracy: 0.9087\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2398 - binary_accuracy: 0.9729 - val_loss: 0.3551 - val_binary_accuracy: 0.9110\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2254 - binary_accuracy: 0.9745 - val_loss: 0.3461 - val_binary_accuracy: 0.9103\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2204 - binary_accuracy: 0.9738 - val_loss: 0.3454 - val_binary_accuracy: 0.9099\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2080 - binary_accuracy: 0.9759 - val_loss: 0.3471 - val_binary_accuracy: 0.9111\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2024 - binary_accuracy: 0.9756 - val_loss: 0.3476 - val_binary_accuracy: 0.9095\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1933 - binary_accuracy: 0.9766 - val_loss: 0.3336 - val_binary_accuracy: 0.9117\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1861 - binary_accuracy: 0.9766 - val_loss: 0.3450 - val_binary_accuracy: 0.9109\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1797 - binary_accuracy: 0.9770 - val_loss: 0.3416 - val_binary_accuracy: 0.9115\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1764 - binary_accuracy: 0.9773 - val_loss: 0.3324 - val_binary_accuracy: 0.9106\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1684 - binary_accuracy: 0.9775 - val_loss: 0.3297 - val_binary_accuracy: 0.9123\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1670 - binary_accuracy: 0.9777 - val_loss: 0.3310 - val_binary_accuracy: 0.9129\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1679 - binary_accuracy: 0.9765 - val_loss: 0.3364 - val_binary_accuracy: 0.9122\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1582 - binary_accuracy: 0.9773 - val_loss: 0.3278 - val_binary_accuracy: 0.9111\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 3s 12ms/step - loss: 0.1539 - binary_accuracy: 0.9781 - val_loss: 0.3147 - val_binary_accuracy: 0.9099\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.1525 - binary_accuracy: 0.9770 - val_loss: 0.3357 - val_binary_accuracy: 0.9127\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1511 - binary_accuracy: 0.9772 - val_loss: 0.3263 - val_binary_accuracy: 0.9148\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.1495 - binary_accuracy: 0.9772 - val_loss: 0.3370 - val_binary_accuracy: 0.9118\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 5s 17ms/step - loss: 0.1477 - binary_accuracy: 0.9779 - val_loss: 0.3281 - val_binary_accuracy: 0.9118\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1472 - binary_accuracy: 0.9780 - val_loss: 0.3195 - val_binary_accuracy: 0.9137\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1446 - binary_accuracy: 0.9773 - val_loss: 0.3261 - val_binary_accuracy: 0.9126\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1357 - binary_accuracy: 0.9787 - val_loss: 0.3260 - val_binary_accuracy: 0.9115\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1409 - binary_accuracy: 0.9769 - val_loss: 0.3358 - val_binary_accuracy: 0.9125\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1379 - binary_accuracy: 0.9778 - val_loss: 0.3272 - val_binary_accuracy: 0.9123\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1366 - binary_accuracy: 0.9768 - val_loss: 0.3232 - val_binary_accuracy: 0.9114\n",
      "93/93 [==============================] - 0s 3ms/step\n",
      "99/99 [==============================] - 1s 7ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 14s 27ms/step - loss: 0.7012 - binary_accuracy: 0.3108 - val_loss: 0.7223 - val_binary_accuracy: 0.1857\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.5669 - binary_accuracy: 0.6047 - val_loss: 0.6799 - val_binary_accuracy: 0.9427\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4865 - binary_accuracy: 0.8023 - val_loss: 0.5400 - val_binary_accuracy: 0.9523\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4327 - binary_accuracy: 0.8516 - val_loss: 0.4692 - val_binary_accuracy: 0.9505\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.3922 - binary_accuracy: 0.8701 - val_loss: 0.4018 - val_binary_accuracy: 0.9532\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3654 - binary_accuracy: 0.8854 - val_loss: 0.3763 - val_binary_accuracy: 0.9540\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3420 - binary_accuracy: 0.8919 - val_loss: 0.3333 - val_binary_accuracy: 0.9540\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.3248 - binary_accuracy: 0.8975 - val_loss: 0.3093 - val_binary_accuracy: 0.9549\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3095 - binary_accuracy: 0.9018 - val_loss: 0.2923 - val_binary_accuracy: 0.9539\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2992 - binary_accuracy: 0.9053 - val_loss: 0.2831 - val_binary_accuracy: 0.9544\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2921 - binary_accuracy: 0.9052 - val_loss: 0.2735 - val_binary_accuracy: 0.9549\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2838 - binary_accuracy: 0.9123 - val_loss: 0.2673 - val_binary_accuracy: 0.9544\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2793 - binary_accuracy: 0.9112 - val_loss: 0.2526 - val_binary_accuracy: 0.9547\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2703 - binary_accuracy: 0.9133 - val_loss: 0.2504 - val_binary_accuracy: 0.9544\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2673 - binary_accuracy: 0.9133 - val_loss: 0.2463 - val_binary_accuracy: 0.9553\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2632 - binary_accuracy: 0.9135 - val_loss: 0.2397 - val_binary_accuracy: 0.9537\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2583 - binary_accuracy: 0.9172 - val_loss: 0.2400 - val_binary_accuracy: 0.9549\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2550 - binary_accuracy: 0.9158 - val_loss: 0.2384 - val_binary_accuracy: 0.9546\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2542 - binary_accuracy: 0.9156 - val_loss: 0.2345 - val_binary_accuracy: 0.9549\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2529 - binary_accuracy: 0.9147 - val_loss: 0.2334 - val_binary_accuracy: 0.9547\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 4s 14ms/step - loss: 0.2480 - binary_accuracy: 0.9181 - val_loss: 0.2273 - val_binary_accuracy: 0.9540\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2484 - binary_accuracy: 0.9159 - val_loss: 0.2300 - val_binary_accuracy: 0.9542\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 4s 14ms/step - loss: 0.2450 - binary_accuracy: 0.9149 - val_loss: 0.2249 - val_binary_accuracy: 0.9539\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2440 - binary_accuracy: 0.9174 - val_loss: 0.2236 - val_binary_accuracy: 0.9544\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2434 - binary_accuracy: 0.9131 - val_loss: 0.2218 - val_binary_accuracy: 0.9542\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2374 - binary_accuracy: 0.9179 - val_loss: 0.2221 - val_binary_accuracy: 0.9550\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2351 - binary_accuracy: 0.9184 - val_loss: 0.2182 - val_binary_accuracy: 0.9544\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2342 - binary_accuracy: 0.9178 - val_loss: 0.2207 - val_binary_accuracy: 0.9544\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2352 - binary_accuracy: 0.9163 - val_loss: 0.2220 - val_binary_accuracy: 0.9559\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2364 - binary_accuracy: 0.9164 - val_loss: 0.2154 - val_binary_accuracy: 0.9551\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2307 - binary_accuracy: 0.9196 - val_loss: 0.2166 - val_binary_accuracy: 0.9550\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2292 - binary_accuracy: 0.9193 - val_loss: 0.2180 - val_binary_accuracy: 0.9554\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2256 - binary_accuracy: 0.9214 - val_loss: 0.2151 - val_binary_accuracy: 0.9564\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2262 - binary_accuracy: 0.9199 - val_loss: 0.2161 - val_binary_accuracy: 0.9557\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2249 - binary_accuracy: 0.9214 - val_loss: 0.2133 - val_binary_accuracy: 0.9559\n",
      "93/93 [==============================] - 1s 7ms/step\n",
      "99/99 [==============================] - 1s 7ms/step\n",
      "87/87 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 26ms/step - loss: 0.5814 - binary_accuracy: 0.6042 - val_loss: 0.7067 - val_binary_accuracy: 0.2781\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4812 - binary_accuracy: 0.8635 - val_loss: 0.6653 - val_binary_accuracy: 0.8760\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4107 - binary_accuracy: 0.9418 - val_loss: 0.5441 - val_binary_accuracy: 0.8959\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.3687 - binary_accuracy: 0.9654 - val_loss: 0.5226 - val_binary_accuracy: 0.8687\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3316 - binary_accuracy: 0.9701 - val_loss: 0.5116 - val_binary_accuracy: 0.8515\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3069 - binary_accuracy: 0.9722 - val_loss: 0.4940 - val_binary_accuracy: 0.8641\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2870 - binary_accuracy: 0.9738 - val_loss: 0.4648 - val_binary_accuracy: 0.8880\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2637 - binary_accuracy: 0.9748 - val_loss: 0.4500 - val_binary_accuracy: 0.8905\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2461 - binary_accuracy: 0.9755 - val_loss: 0.4437 - val_binary_accuracy: 0.8983\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2348 - binary_accuracy: 0.9754 - val_loss: 0.4350 - val_binary_accuracy: 0.8959\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2211 - binary_accuracy: 0.9770 - val_loss: 0.4253 - val_binary_accuracy: 0.9023\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2111 - binary_accuracy: 0.9769 - val_loss: 0.4249 - val_binary_accuracy: 0.9003\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2024 - binary_accuracy: 0.9759 - val_loss: 0.4159 - val_binary_accuracy: 0.9043\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1911 - binary_accuracy: 0.9766 - val_loss: 0.4071 - val_binary_accuracy: 0.9087\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 4s 15ms/step - loss: 0.1839 - binary_accuracy: 0.9783 - val_loss: 0.4110 - val_binary_accuracy: 0.9057\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.1794 - binary_accuracy: 0.9772 - val_loss: 0.4201 - val_binary_accuracy: 0.8977\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.1740 - binary_accuracy: 0.9763 - val_loss: 0.3957 - val_binary_accuracy: 0.9104\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.1686 - binary_accuracy: 0.9774 - val_loss: 0.3982 - val_binary_accuracy: 0.9095\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1651 - binary_accuracy: 0.9771 - val_loss: 0.3884 - val_binary_accuracy: 0.9117\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1578 - binary_accuracy: 0.9785 - val_loss: 0.4022 - val_binary_accuracy: 0.9079\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1561 - binary_accuracy: 0.9773 - val_loss: 0.3859 - val_binary_accuracy: 0.9110\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1503 - binary_accuracy: 0.9779 - val_loss: 0.3808 - val_binary_accuracy: 0.9121\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1490 - binary_accuracy: 0.9775 - val_loss: 0.3894 - val_binary_accuracy: 0.9087\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1507 - binary_accuracy: 0.9774 - val_loss: 0.4045 - val_binary_accuracy: 0.9067\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1440 - binary_accuracy: 0.9781 - val_loss: 0.3853 - val_binary_accuracy: 0.9115\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1475 - binary_accuracy: 0.9765 - val_loss: 0.3694 - val_binary_accuracy: 0.9118\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1413 - binary_accuracy: 0.9785 - val_loss: 0.3848 - val_binary_accuracy: 0.9118\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1403 - binary_accuracy: 0.9775 - val_loss: 0.4040 - val_binary_accuracy: 0.9001\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1338 - binary_accuracy: 0.9782 - val_loss: 0.3845 - val_binary_accuracy: 0.9109\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1340 - binary_accuracy: 0.9787 - val_loss: 0.3725 - val_binary_accuracy: 0.9125\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1324 - binary_accuracy: 0.9768 - val_loss: 0.4103 - val_binary_accuracy: 0.8935\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1313 - binary_accuracy: 0.9771 - val_loss: 0.3492 - val_binary_accuracy: 0.9127\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1283 - binary_accuracy: 0.9774 - val_loss: 0.3870 - val_binary_accuracy: 0.9037\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1305 - binary_accuracy: 0.9770 - val_loss: 0.3781 - val_binary_accuracy: 0.9090\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1271 - binary_accuracy: 0.9782 - val_loss: 0.3683 - val_binary_accuracy: 0.9115\n",
      "93/93 [==============================] - 1s 7ms/step\n",
      "99/99 [==============================] - 1s 7ms/step\n",
      "87/87 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 14s 24ms/step - loss: 0.6128 - binary_accuracy: 0.6770 - val_loss: 0.6995 - val_binary_accuracy: 0.8136\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.5041 - binary_accuracy: 0.8450 - val_loss: 0.6143 - val_binary_accuracy: 0.8950\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.4414 - binary_accuracy: 0.8775 - val_loss: 0.4713 - val_binary_accuracy: 0.9505\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3940 - binary_accuracy: 0.8933 - val_loss: 0.4106 - val_binary_accuracy: 0.9540\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3634 - binary_accuracy: 0.9014 - val_loss: 0.3669 - val_binary_accuracy: 0.9546\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.3387 - binary_accuracy: 0.9068 - val_loss: 0.3499 - val_binary_accuracy: 0.9536\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3213 - binary_accuracy: 0.9100 - val_loss: 0.3177 - val_binary_accuracy: 0.9544\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3111 - binary_accuracy: 0.9090 - val_loss: 0.3063 - val_binary_accuracy: 0.9529\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2958 - binary_accuracy: 0.9141 - val_loss: 0.2911 - val_binary_accuracy: 0.9546\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 4s 16ms/step - loss: 0.2865 - binary_accuracy: 0.9132 - val_loss: 0.2928 - val_binary_accuracy: 0.9544\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.2804 - binary_accuracy: 0.9138 - val_loss: 0.2800 - val_binary_accuracy: 0.9539\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.2743 - binary_accuracy: 0.9146 - val_loss: 0.2739 - val_binary_accuracy: 0.9539\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2726 - binary_accuracy: 0.9119 - val_loss: 0.2738 - val_binary_accuracy: 0.9542\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.2688 - binary_accuracy: 0.9156 - val_loss: 0.2661 - val_binary_accuracy: 0.9536\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2638 - binary_accuracy: 0.9148 - val_loss: 0.2639 - val_binary_accuracy: 0.9536\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2597 - binary_accuracy: 0.9158 - val_loss: 0.2594 - val_binary_accuracy: 0.9540\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2533 - binary_accuracy: 0.9175 - val_loss: 0.2608 - val_binary_accuracy: 0.9543\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2514 - binary_accuracy: 0.9183 - val_loss: 0.2603 - val_binary_accuracy: 0.9551\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2504 - binary_accuracy: 0.9165 - val_loss: 0.2548 - val_binary_accuracy: 0.9549\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2473 - binary_accuracy: 0.9159 - val_loss: 0.2505 - val_binary_accuracy: 0.9549\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2474 - binary_accuracy: 0.9158 - val_loss: 0.2523 - val_binary_accuracy: 0.9557\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2433 - binary_accuracy: 0.9170 - val_loss: 0.2491 - val_binary_accuracy: 0.9554\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2411 - binary_accuracy: 0.9178 - val_loss: 0.2458 - val_binary_accuracy: 0.9549\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2400 - binary_accuracy: 0.9169 - val_loss: 0.2449 - val_binary_accuracy: 0.9554\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2422 - binary_accuracy: 0.9159 - val_loss: 0.2447 - val_binary_accuracy: 0.9560\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2389 - binary_accuracy: 0.9179 - val_loss: 0.2407 - val_binary_accuracy: 0.9557\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2330 - binary_accuracy: 0.9210 - val_loss: 0.2395 - val_binary_accuracy: 0.9563\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2348 - binary_accuracy: 0.9191 - val_loss: 0.2420 - val_binary_accuracy: 0.9564\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2345 - binary_accuracy: 0.9138 - val_loss: 0.2370 - val_binary_accuracy: 0.9561\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2305 - binary_accuracy: 0.9197 - val_loss: 0.2354 - val_binary_accuracy: 0.9568\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2301 - binary_accuracy: 0.9188 - val_loss: 0.2318 - val_binary_accuracy: 0.9563\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2323 - binary_accuracy: 0.9174 - val_loss: 0.2317 - val_binary_accuracy: 0.9561\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2310 - binary_accuracy: 0.9174 - val_loss: 0.2288 - val_binary_accuracy: 0.9554\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2244 - binary_accuracy: 0.9194 - val_loss: 0.2284 - val_binary_accuracy: 0.9563\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2255 - binary_accuracy: 0.9206 - val_loss: 0.2268 - val_binary_accuracy: 0.9567\n",
      "93/93 [==============================] - 1s 4ms/step\n",
      "99/99 [==============================] - 1s 5ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 12s 23ms/step - loss: 0.8091 - binary_accuracy: 0.6298 - val_loss: 0.6944 - val_binary_accuracy: 0.7294\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.6095 - binary_accuracy: 0.8644 - val_loss: 0.6388 - val_binary_accuracy: 0.7878\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4952 - binary_accuracy: 0.9349 - val_loss: 0.5284 - val_binary_accuracy: 0.8930\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4328 - binary_accuracy: 0.9579 - val_loss: 0.5005 - val_binary_accuracy: 0.8953\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3809 - binary_accuracy: 0.9652 - val_loss: 0.4742 - val_binary_accuracy: 0.8955\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.3455 - binary_accuracy: 0.9709 - val_loss: 0.4423 - val_binary_accuracy: 0.9045\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.3207 - binary_accuracy: 0.9711 - val_loss: 0.4298 - val_binary_accuracy: 0.9020\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.3005 - binary_accuracy: 0.9717 - val_loss: 0.4140 - val_binary_accuracy: 0.9043\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 5s 17ms/step - loss: 0.2811 - binary_accuracy: 0.9720 - val_loss: 0.4050 - val_binary_accuracy: 0.9058\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2620 - binary_accuracy: 0.9750 - val_loss: 0.4019 - val_binary_accuracy: 0.9007\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2460 - binary_accuracy: 0.9759 - val_loss: 0.3952 - val_binary_accuracy: 0.9056\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2405 - binary_accuracy: 0.9738 - val_loss: 0.3848 - val_binary_accuracy: 0.9062\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2250 - binary_accuracy: 0.9761 - val_loss: 0.3671 - val_binary_accuracy: 0.9072\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2197 - binary_accuracy: 0.9749 - val_loss: 0.3573 - val_binary_accuracy: 0.9087\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2102 - binary_accuracy: 0.9757 - val_loss: 0.3653 - val_binary_accuracy: 0.9064\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1998 - binary_accuracy: 0.9769 - val_loss: 0.3492 - val_binary_accuracy: 0.9080\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1968 - binary_accuracy: 0.9756 - val_loss: 0.3490 - val_binary_accuracy: 0.9098\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1864 - binary_accuracy: 0.9764 - val_loss: 0.3417 - val_binary_accuracy: 0.9107\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1804 - binary_accuracy: 0.9760 - val_loss: 0.3471 - val_binary_accuracy: 0.9099\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1748 - binary_accuracy: 0.9767 - val_loss: 0.3458 - val_binary_accuracy: 0.9098\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1690 - binary_accuracy: 0.9782 - val_loss: 0.3413 - val_binary_accuracy: 0.9088\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1720 - binary_accuracy: 0.9762 - val_loss: 0.3337 - val_binary_accuracy: 0.9103\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1652 - binary_accuracy: 0.9766 - val_loss: 0.3530 - val_binary_accuracy: 0.9058\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1585 - binary_accuracy: 0.9768 - val_loss: 0.3441 - val_binary_accuracy: 0.9076\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1531 - binary_accuracy: 0.9769 - val_loss: 0.3420 - val_binary_accuracy: 0.9065\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1538 - binary_accuracy: 0.9763 - val_loss: 0.3375 - val_binary_accuracy: 0.9085\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1490 - binary_accuracy: 0.9776 - val_loss: 0.3377 - val_binary_accuracy: 0.9081\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1505 - binary_accuracy: 0.9760 - val_loss: 0.3324 - val_binary_accuracy: 0.9090\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1462 - binary_accuracy: 0.9767 - val_loss: 0.3330 - val_binary_accuracy: 0.9100\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1458 - binary_accuracy: 0.9774 - val_loss: 0.3482 - val_binary_accuracy: 0.9042\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1419 - binary_accuracy: 0.9772 - val_loss: 0.3305 - val_binary_accuracy: 0.9100\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1396 - binary_accuracy: 0.9766 - val_loss: 0.3356 - val_binary_accuracy: 0.9075\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1369 - binary_accuracy: 0.9772 - val_loss: 0.3374 - val_binary_accuracy: 0.9071\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1352 - binary_accuracy: 0.9777 - val_loss: 0.3353 - val_binary_accuracy: 0.9077\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1339 - binary_accuracy: 0.9773 - val_loss: 0.3367 - val_binary_accuracy: 0.9067\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 12s 22ms/step - loss: 0.6253 - binary_accuracy: 0.4672 - val_loss: 0.7142 - val_binary_accuracy: 0.1898\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.5181 - binary_accuracy: 0.7532 - val_loss: 0.6576 - val_binary_accuracy: 0.9374\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4528 - binary_accuracy: 0.8469 - val_loss: 0.5229 - val_binary_accuracy: 0.9523\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 4s 14ms/step - loss: 0.4046 - binary_accuracy: 0.8780 - val_loss: 0.5183 - val_binary_accuracy: 0.9526\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.3711 - binary_accuracy: 0.8838 - val_loss: 0.4561 - val_binary_accuracy: 0.9537\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.3452 - binary_accuracy: 0.8988 - val_loss: 0.4201 - val_binary_accuracy: 0.9539\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3262 - binary_accuracy: 0.9026 - val_loss: 0.4141 - val_binary_accuracy: 0.9546\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.3125 - binary_accuracy: 0.9073 - val_loss: 0.4055 - val_binary_accuracy: 0.9549\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2986 - binary_accuracy: 0.9092 - val_loss: 0.3879 - val_binary_accuracy: 0.9534\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2885 - binary_accuracy: 0.9129 - val_loss: 0.3822 - val_binary_accuracy: 0.9543\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2818 - binary_accuracy: 0.9110 - val_loss: 0.3808 - val_binary_accuracy: 0.9524\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2743 - binary_accuracy: 0.9160 - val_loss: 0.3701 - val_binary_accuracy: 0.9520\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2698 - binary_accuracy: 0.9137 - val_loss: 0.3761 - val_binary_accuracy: 0.9529\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2626 - binary_accuracy: 0.9168 - val_loss: 0.3721 - val_binary_accuracy: 0.9522\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2606 - binary_accuracy: 0.9163 - val_loss: 0.3658 - val_binary_accuracy: 0.9515\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2575 - binary_accuracy: 0.9170 - val_loss: 0.3659 - val_binary_accuracy: 0.9523\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2545 - binary_accuracy: 0.9188 - val_loss: 0.3614 - val_binary_accuracy: 0.9529\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2531 - binary_accuracy: 0.9179 - val_loss: 0.3585 - val_binary_accuracy: 0.9530\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2483 - binary_accuracy: 0.9197 - val_loss: 0.3546 - val_binary_accuracy: 0.9515\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2435 - binary_accuracy: 0.9198 - val_loss: 0.3570 - val_binary_accuracy: 0.9530\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2418 - binary_accuracy: 0.9200 - val_loss: 0.3579 - val_binary_accuracy: 0.9523\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2387 - binary_accuracy: 0.9192 - val_loss: 0.3554 - val_binary_accuracy: 0.9536\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2362 - binary_accuracy: 0.9201 - val_loss: 0.3489 - val_binary_accuracy: 0.9536\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2356 - binary_accuracy: 0.9193 - val_loss: 0.3503 - val_binary_accuracy: 0.9524\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2338 - binary_accuracy: 0.9222 - val_loss: 0.3491 - val_binary_accuracy: 0.9537\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2308 - binary_accuracy: 0.9221 - val_loss: 0.3476 - val_binary_accuracy: 0.9542\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2306 - binary_accuracy: 0.9205 - val_loss: 0.3426 - val_binary_accuracy: 0.9534\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2313 - binary_accuracy: 0.9217 - val_loss: 0.3424 - val_binary_accuracy: 0.9534\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2268 - binary_accuracy: 0.9228 - val_loss: 0.3458 - val_binary_accuracy: 0.9537\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2253 - binary_accuracy: 0.9242 - val_loss: 0.3427 - val_binary_accuracy: 0.9543\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2256 - binary_accuracy: 0.9234 - val_loss: 0.3391 - val_binary_accuracy: 0.9540\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2253 - binary_accuracy: 0.9224 - val_loss: 0.3354 - val_binary_accuracy: 0.9539\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2269 - binary_accuracy: 0.9216 - val_loss: 0.3328 - val_binary_accuracy: 0.9546\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2229 - binary_accuracy: 0.9233 - val_loss: 0.3328 - val_binary_accuracy: 0.9546\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2214 - binary_accuracy: 0.9214 - val_loss: 0.3287 - val_binary_accuracy: 0.9544\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 11s 14ms/step - loss: 0.8361 - binary_accuracy: 0.5543 - val_loss: 0.7033 - val_binary_accuracy: 0.7296\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.6376 - binary_accuracy: 0.8237 - val_loss: 0.6704 - val_binary_accuracy: 0.8073\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 4s 13ms/step - loss: 0.5215 - binary_accuracy: 0.9233 - val_loss: 0.5771 - val_binary_accuracy: 0.8848\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.4498 - binary_accuracy: 0.9491 - val_loss: 0.5374 - val_binary_accuracy: 0.8875\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.4039 - binary_accuracy: 0.9607 - val_loss: 0.5220 - val_binary_accuracy: 0.8969\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3670 - binary_accuracy: 0.9675 - val_loss: 0.4951 - val_binary_accuracy: 0.9027\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.3388 - binary_accuracy: 0.9702 - val_loss: 0.4939 - val_binary_accuracy: 0.9065\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3159 - binary_accuracy: 0.9720 - val_loss: 0.4494 - val_binary_accuracy: 0.9067\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2948 - binary_accuracy: 0.9726 - val_loss: 0.4500 - val_binary_accuracy: 0.9056\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2805 - binary_accuracy: 0.9750 - val_loss: 0.4393 - val_binary_accuracy: 0.9052\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2630 - binary_accuracy: 0.9742 - val_loss: 0.4187 - val_binary_accuracy: 0.9098\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2514 - binary_accuracy: 0.9763 - val_loss: 0.4206 - val_binary_accuracy: 0.9107\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2418 - binary_accuracy: 0.9758 - val_loss: 0.4035 - val_binary_accuracy: 0.9098\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2351 - binary_accuracy: 0.9762 - val_loss: 0.3967 - val_binary_accuracy: 0.9119\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2231 - binary_accuracy: 0.9752 - val_loss: 0.3892 - val_binary_accuracy: 0.9111\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2196 - binary_accuracy: 0.9746 - val_loss: 0.3893 - val_binary_accuracy: 0.9117\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2098 - binary_accuracy: 0.9758 - val_loss: 0.3633 - val_binary_accuracy: 0.9094\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2019 - binary_accuracy: 0.9759 - val_loss: 0.3712 - val_binary_accuracy: 0.9117\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1957 - binary_accuracy: 0.9759 - val_loss: 0.3626 - val_binary_accuracy: 0.9092\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.1912 - binary_accuracy: 0.9756 - val_loss: 0.3681 - val_binary_accuracy: 0.9115\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1865 - binary_accuracy: 0.9763 - val_loss: 0.3566 - val_binary_accuracy: 0.9106\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1825 - binary_accuracy: 0.9765 - val_loss: 0.3527 - val_binary_accuracy: 0.9111\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 7s 23ms/step - loss: 0.1790 - binary_accuracy: 0.9761 - val_loss: 0.3646 - val_binary_accuracy: 0.9096\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1704 - binary_accuracy: 0.9777 - val_loss: 0.3606 - val_binary_accuracy: 0.9088\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1716 - binary_accuracy: 0.9775 - val_loss: 0.3467 - val_binary_accuracy: 0.9111\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.1687 - binary_accuracy: 0.9767 - val_loss: 0.3413 - val_binary_accuracy: 0.9113\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1610 - binary_accuracy: 0.9779 - val_loss: 0.3391 - val_binary_accuracy: 0.9126\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1599 - binary_accuracy: 0.9770 - val_loss: 0.3446 - val_binary_accuracy: 0.9110\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.1575 - binary_accuracy: 0.9774 - val_loss: 0.3439 - val_binary_accuracy: 0.9096\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1579 - binary_accuracy: 0.9778 - val_loss: 0.3489 - val_binary_accuracy: 0.9098\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1561 - binary_accuracy: 0.9763 - val_loss: 0.3568 - val_binary_accuracy: 0.9072\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1508 - binary_accuracy: 0.9760 - val_loss: 0.3333 - val_binary_accuracy: 0.9106\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 3s 12ms/step - loss: 0.1503 - binary_accuracy: 0.9754 - val_loss: 0.3372 - val_binary_accuracy: 0.9091\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.1489 - binary_accuracy: 0.9767 - val_loss: 0.3337 - val_binary_accuracy: 0.9121\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1476 - binary_accuracy: 0.9766 - val_loss: 0.3386 - val_binary_accuracy: 0.9113\n",
      "93/93 [==============================] - 1s 7ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 14s 26ms/step - loss: 0.6678 - binary_accuracy: 0.6457 - val_loss: 0.6839 - val_binary_accuracy: 0.8194\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.5380 - binary_accuracy: 0.8341 - val_loss: 0.5815 - val_binary_accuracy: 0.8688\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.4579 - binary_accuracy: 0.8743 - val_loss: 0.4407 - val_binary_accuracy: 0.9502\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.4079 - binary_accuracy: 0.8924 - val_loss: 0.3843 - val_binary_accuracy: 0.9527\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.3747 - binary_accuracy: 0.8978 - val_loss: 0.3476 - val_binary_accuracy: 0.9530\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3460 - binary_accuracy: 0.9046 - val_loss: 0.3153 - val_binary_accuracy: 0.9527\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3305 - binary_accuracy: 0.9048 - val_loss: 0.2901 - val_binary_accuracy: 0.9530\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 7s 23ms/step - loss: 0.3136 - binary_accuracy: 0.9115 - val_loss: 0.2696 - val_binary_accuracy: 0.9530\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3010 - binary_accuracy: 0.9090 - val_loss: 0.2623 - val_binary_accuracy: 0.9539\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2971 - binary_accuracy: 0.9084 - val_loss: 0.2432 - val_binary_accuracy: 0.9532\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2867 - binary_accuracy: 0.9103 - val_loss: 0.2380 - val_binary_accuracy: 0.9539\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2791 - binary_accuracy: 0.9112 - val_loss: 0.2358 - val_binary_accuracy: 0.9540\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2714 - binary_accuracy: 0.9142 - val_loss: 0.2281 - val_binary_accuracy: 0.9542\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2732 - binary_accuracy: 0.9120 - val_loss: 0.2226 - val_binary_accuracy: 0.9542\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2649 - binary_accuracy: 0.9160 - val_loss: 0.2223 - val_binary_accuracy: 0.9539\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2622 - binary_accuracy: 0.9159 - val_loss: 0.2221 - val_binary_accuracy: 0.9539\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2599 - binary_accuracy: 0.9156 - val_loss: 0.2166 - val_binary_accuracy: 0.9544\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2512 - binary_accuracy: 0.9150 - val_loss: 0.2171 - val_binary_accuracy: 0.9540\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2532 - binary_accuracy: 0.9150 - val_loss: 0.2128 - val_binary_accuracy: 0.9543\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2498 - binary_accuracy: 0.9168 - val_loss: 0.2121 - val_binary_accuracy: 0.9547\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2477 - binary_accuracy: 0.9168 - val_loss: 0.2114 - val_binary_accuracy: 0.9550\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2439 - binary_accuracy: 0.9179 - val_loss: 0.2082 - val_binary_accuracy: 0.9557\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2470 - binary_accuracy: 0.9136 - val_loss: 0.2059 - val_binary_accuracy: 0.9549\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2437 - binary_accuracy: 0.9161 - val_loss: 0.2079 - val_binary_accuracy: 0.9556\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2402 - binary_accuracy: 0.9169 - val_loss: 0.2053 - val_binary_accuracy: 0.9550\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 4s 13ms/step - loss: 0.2425 - binary_accuracy: 0.9175 - val_loss: 0.2059 - val_binary_accuracy: 0.9550\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.2381 - binary_accuracy: 0.9172 - val_loss: 0.2020 - val_binary_accuracy: 0.9557\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 3s 12ms/step - loss: 0.2362 - binary_accuracy: 0.9192 - val_loss: 0.2025 - val_binary_accuracy: 0.9556\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2345 - binary_accuracy: 0.9191 - val_loss: 0.2011 - val_binary_accuracy: 0.9554\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2324 - binary_accuracy: 0.9208 - val_loss: 0.2025 - val_binary_accuracy: 0.9559\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2307 - binary_accuracy: 0.9198 - val_loss: 0.2002 - val_binary_accuracy: 0.9564\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2349 - binary_accuracy: 0.9174 - val_loss: 0.2026 - val_binary_accuracy: 0.9559\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2278 - binary_accuracy: 0.9204 - val_loss: 0.2021 - val_binary_accuracy: 0.9559\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2311 - binary_accuracy: 0.9160 - val_loss: 0.1991 - val_binary_accuracy: 0.9563\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2291 - binary_accuracy: 0.9173 - val_loss: 0.1995 - val_binary_accuracy: 0.9571\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 7ms/step\n",
      "87/87 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 24ms/step - loss: 1.1017 - binary_accuracy: 0.7848 - val_loss: 0.6991 - val_binary_accuracy: 0.7266\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.7861 - binary_accuracy: 0.8819 - val_loss: 0.6409 - val_binary_accuracy: 0.7993\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.6099 - binary_accuracy: 0.9157 - val_loss: 0.5520 - val_binary_accuracy: 0.8747\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4948 - binary_accuracy: 0.9381 - val_loss: 0.4935 - val_binary_accuracy: 0.9014\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4252 - binary_accuracy: 0.9481 - val_loss: 0.4689 - val_binary_accuracy: 0.9102\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.3760 - binary_accuracy: 0.9576 - val_loss: 0.4605 - val_binary_accuracy: 0.9114\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3408 - binary_accuracy: 0.9638 - val_loss: 0.4237 - val_binary_accuracy: 0.9152\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3100 - binary_accuracy: 0.9663 - val_loss: 0.4024 - val_binary_accuracy: 0.9178\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2937 - binary_accuracy: 0.9682 - val_loss: 0.3881 - val_binary_accuracy: 0.9195\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2709 - binary_accuracy: 0.9704 - val_loss: 0.3783 - val_binary_accuracy: 0.9199\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2634 - binary_accuracy: 0.9699 - val_loss: 0.3860 - val_binary_accuracy: 0.9193\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2461 - binary_accuracy: 0.9723 - val_loss: 0.3898 - val_binary_accuracy: 0.9199\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2365 - binary_accuracy: 0.9732 - val_loss: 0.3668 - val_binary_accuracy: 0.9206\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2244 - binary_accuracy: 0.9732 - val_loss: 0.3466 - val_binary_accuracy: 0.9211\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2197 - binary_accuracy: 0.9732 - val_loss: 0.3450 - val_binary_accuracy: 0.9202\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.2080 - binary_accuracy: 0.9748 - val_loss: 0.3453 - val_binary_accuracy: 0.9210\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2014 - binary_accuracy: 0.9742 - val_loss: 0.3407 - val_binary_accuracy: 0.9175\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1905 - binary_accuracy: 0.9745 - val_loss: 0.3339 - val_binary_accuracy: 0.9194\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.1837 - binary_accuracy: 0.9756 - val_loss: 0.3311 - val_binary_accuracy: 0.9205\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 4s 14ms/step - loss: 0.1795 - binary_accuracy: 0.9754 - val_loss: 0.3284 - val_binary_accuracy: 0.9209\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.1777 - binary_accuracy: 0.9750 - val_loss: 0.3079 - val_binary_accuracy: 0.9211\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 5s 17ms/step - loss: 0.1717 - binary_accuracy: 0.9738 - val_loss: 0.3282 - val_binary_accuracy: 0.9160\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1684 - binary_accuracy: 0.9754 - val_loss: 0.3199 - val_binary_accuracy: 0.9205\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.1621 - binary_accuracy: 0.9756 - val_loss: 0.3124 - val_binary_accuracy: 0.9193\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1593 - binary_accuracy: 0.9760 - val_loss: 0.3108 - val_binary_accuracy: 0.9190\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1543 - binary_accuracy: 0.9754 - val_loss: 0.3094 - val_binary_accuracy: 0.9195\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1542 - binary_accuracy: 0.9757 - val_loss: 0.3136 - val_binary_accuracy: 0.9191\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1513 - binary_accuracy: 0.9741 - val_loss: 0.3219 - val_binary_accuracy: 0.9148\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1507 - binary_accuracy: 0.9753 - val_loss: 0.2994 - val_binary_accuracy: 0.9205\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1465 - binary_accuracy: 0.9746 - val_loss: 0.3018 - val_binary_accuracy: 0.9205\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1410 - binary_accuracy: 0.9762 - val_loss: 0.3067 - val_binary_accuracy: 0.9190\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1456 - binary_accuracy: 0.9757 - val_loss: 0.3089 - val_binary_accuracy: 0.9178\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1403 - binary_accuracy: 0.9755 - val_loss: 0.3043 - val_binary_accuracy: 0.9195\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1366 - binary_accuracy: 0.9767 - val_loss: 0.3038 - val_binary_accuracy: 0.9187\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1401 - binary_accuracy: 0.9750 - val_loss: 0.3240 - val_binary_accuracy: 0.9102\n",
      "93/93 [==============================] - 1s 6ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 12s 22ms/step - loss: 0.6648 - binary_accuracy: 0.4486 - val_loss: 0.7156 - val_binary_accuracy: 0.1899\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.5388 - binary_accuracy: 0.7525 - val_loss: 0.6541 - val_binary_accuracy: 0.9370\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.4686 - binary_accuracy: 0.8454 - val_loss: 0.5039 - val_binary_accuracy: 0.9546\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.4268 - binary_accuracy: 0.8691 - val_loss: 0.4443 - val_binary_accuracy: 0.9556\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3891 - binary_accuracy: 0.8816 - val_loss: 0.3933 - val_binary_accuracy: 0.9563\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.3637 - binary_accuracy: 0.8914 - val_loss: 0.3656 - val_binary_accuracy: 0.9580\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3438 - binary_accuracy: 0.8979 - val_loss: 0.3384 - val_binary_accuracy: 0.9578\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3277 - binary_accuracy: 0.9013 - val_loss: 0.3309 - val_binary_accuracy: 0.9584\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.3176 - binary_accuracy: 0.9011 - val_loss: 0.3117 - val_binary_accuracy: 0.9583\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3039 - binary_accuracy: 0.9069 - val_loss: 0.2941 - val_binary_accuracy: 0.9578\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2957 - binary_accuracy: 0.9089 - val_loss: 0.2880 - val_binary_accuracy: 0.9576\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2898 - binary_accuracy: 0.9109 - val_loss: 0.2802 - val_binary_accuracy: 0.9583\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2832 - binary_accuracy: 0.9089 - val_loss: 0.2738 - val_binary_accuracy: 0.9581\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2776 - binary_accuracy: 0.9114 - val_loss: 0.2637 - val_binary_accuracy: 0.9587\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2746 - binary_accuracy: 0.9117 - val_loss: 0.2621 - val_binary_accuracy: 0.9586\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2691 - binary_accuracy: 0.9142 - val_loss: 0.2657 - val_binary_accuracy: 0.9587\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2661 - binary_accuracy: 0.9131 - val_loss: 0.2539 - val_binary_accuracy: 0.9586\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.2601 - binary_accuracy: 0.9156 - val_loss: 0.2529 - val_binary_accuracy: 0.9588\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2595 - binary_accuracy: 0.9127 - val_loss: 0.2502 - val_binary_accuracy: 0.9587\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2573 - binary_accuracy: 0.9155 - val_loss: 0.2470 - val_binary_accuracy: 0.9593\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2532 - binary_accuracy: 0.9165 - val_loss: 0.2438 - val_binary_accuracy: 0.9590\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 5s 17ms/step - loss: 0.2494 - binary_accuracy: 0.9140 - val_loss: 0.2426 - val_binary_accuracy: 0.9593\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2504 - binary_accuracy: 0.9162 - val_loss: 0.2433 - val_binary_accuracy: 0.9594\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.2475 - binary_accuracy: 0.9171 - val_loss: 0.2338 - val_binary_accuracy: 0.9591\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2437 - binary_accuracy: 0.9170 - val_loss: 0.2344 - val_binary_accuracy: 0.9590\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2422 - binary_accuracy: 0.9155 - val_loss: 0.2319 - val_binary_accuracy: 0.9591\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2391 - binary_accuracy: 0.9194 - val_loss: 0.2311 - val_binary_accuracy: 0.9593\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2417 - binary_accuracy: 0.9137 - val_loss: 0.2269 - val_binary_accuracy: 0.9603\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2361 - binary_accuracy: 0.9176 - val_loss: 0.2279 - val_binary_accuracy: 0.9600\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2381 - binary_accuracy: 0.9153 - val_loss: 0.2247 - val_binary_accuracy: 0.9605\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2338 - binary_accuracy: 0.9188 - val_loss: 0.2247 - val_binary_accuracy: 0.9600\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2327 - binary_accuracy: 0.9171 - val_loss: 0.2202 - val_binary_accuracy: 0.9611\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2345 - binary_accuracy: 0.9177 - val_loss: 0.2195 - val_binary_accuracy: 0.9600\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2340 - binary_accuracy: 0.9177 - val_loss: 0.2170 - val_binary_accuracy: 0.9608\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2296 - binary_accuracy: 0.9176 - val_loss: 0.2151 - val_binary_accuracy: 0.9607\n",
      "93/93 [==============================] - 1s 7ms/step\n",
      "99/99 [==============================] - 1s 7ms/step\n",
      "87/87 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 23ms/step - loss: 0.6204 - binary_accuracy: 0.6736 - val_loss: 0.6959 - val_binary_accuracy: 0.7232\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.5065 - binary_accuracy: 0.8859 - val_loss: 0.6368 - val_binary_accuracy: 0.8244\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4402 - binary_accuracy: 0.9420 - val_loss: 0.5434 - val_binary_accuracy: 0.8816\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3915 - binary_accuracy: 0.9591 - val_loss: 0.5248 - val_binary_accuracy: 0.8911\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3545 - binary_accuracy: 0.9672 - val_loss: 0.5162 - val_binary_accuracy: 0.8981\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3275 - binary_accuracy: 0.9718 - val_loss: 0.4953 - val_binary_accuracy: 0.8935\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3020 - binary_accuracy: 0.9727 - val_loss: 0.4629 - val_binary_accuracy: 0.9083\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2857 - binary_accuracy: 0.9727 - val_loss: 0.4453 - val_binary_accuracy: 0.9016\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2669 - binary_accuracy: 0.9729 - val_loss: 0.4399 - val_binary_accuracy: 0.9068\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2576 - binary_accuracy: 0.9725 - val_loss: 0.4303 - val_binary_accuracy: 0.9079\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2442 - binary_accuracy: 0.9745 - val_loss: 0.4161 - val_binary_accuracy: 0.9085\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2359 - binary_accuracy: 0.9743 - val_loss: 0.4144 - val_binary_accuracy: 0.9085\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2227 - binary_accuracy: 0.9748 - val_loss: 0.4004 - val_binary_accuracy: 0.9095\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2114 - binary_accuracy: 0.9754 - val_loss: 0.3917 - val_binary_accuracy: 0.9080\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2086 - binary_accuracy: 0.9755 - val_loss: 0.4054 - val_binary_accuracy: 0.9091\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 3s 12ms/step - loss: 0.1985 - binary_accuracy: 0.9758 - val_loss: 0.3840 - val_binary_accuracy: 0.9110\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.1945 - binary_accuracy: 0.9773 - val_loss: 0.3900 - val_binary_accuracy: 0.9102\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 4s 15ms/step - loss: 0.1849 - binary_accuracy: 0.9779 - val_loss: 0.3734 - val_binary_accuracy: 0.9106\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1817 - binary_accuracy: 0.9762 - val_loss: 0.3697 - val_binary_accuracy: 0.9115\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1777 - binary_accuracy: 0.9758 - val_loss: 0.3779 - val_binary_accuracy: 0.9111\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1682 - binary_accuracy: 0.9773 - val_loss: 0.3687 - val_binary_accuracy: 0.9106\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1717 - binary_accuracy: 0.9751 - val_loss: 0.3585 - val_binary_accuracy: 0.9098\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1672 - binary_accuracy: 0.9763 - val_loss: 0.3768 - val_binary_accuracy: 0.9104\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1637 - binary_accuracy: 0.9778 - val_loss: 0.3622 - val_binary_accuracy: 0.9107\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1590 - binary_accuracy: 0.9778 - val_loss: 0.3684 - val_binary_accuracy: 0.9106\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1529 - binary_accuracy: 0.9774 - val_loss: 0.3699 - val_binary_accuracy: 0.9100\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1554 - binary_accuracy: 0.9774 - val_loss: 0.3804 - val_binary_accuracy: 0.9096\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1558 - binary_accuracy: 0.9768 - val_loss: 0.3558 - val_binary_accuracy: 0.9119\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.1554 - binary_accuracy: 0.9755 - val_loss: 0.3644 - val_binary_accuracy: 0.9102\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1427 - binary_accuracy: 0.9777 - val_loss: 0.3754 - val_binary_accuracy: 0.9081\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.1467 - binary_accuracy: 0.9750 - val_loss: 0.3622 - val_binary_accuracy: 0.9106\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1454 - binary_accuracy: 0.9771 - val_loss: 0.3620 - val_binary_accuracy: 0.9104\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1456 - binary_accuracy: 0.9764 - val_loss: 0.3533 - val_binary_accuracy: 0.9115\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.1431 - binary_accuracy: 0.9773 - val_loss: 0.3639 - val_binary_accuracy: 0.9104\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1401 - binary_accuracy: 0.9772 - val_loss: 0.3542 - val_binary_accuracy: 0.9106\n",
      "93/93 [==============================] - 1s 5ms/step\n",
      "99/99 [==============================] - 1s 6ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 14s 23ms/step - loss: 0.6340 - binary_accuracy: 0.6887 - val_loss: 0.6914 - val_binary_accuracy: 0.8077\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.5106 - binary_accuracy: 0.8419 - val_loss: 0.5860 - val_binary_accuracy: 0.8731\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4436 - binary_accuracy: 0.8734 - val_loss: 0.4080 - val_binary_accuracy: 0.9495\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3946 - binary_accuracy: 0.8897 - val_loss: 0.3597 - val_binary_accuracy: 0.9524\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3653 - binary_accuracy: 0.8962 - val_loss: 0.3233 - val_binary_accuracy: 0.9520\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3436 - binary_accuracy: 0.9020 - val_loss: 0.2947 - val_binary_accuracy: 0.9517\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3272 - binary_accuracy: 0.9034 - val_loss: 0.2807 - val_binary_accuracy: 0.9507\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3095 - binary_accuracy: 0.9092 - val_loss: 0.2673 - val_binary_accuracy: 0.9532\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.3012 - binary_accuracy: 0.9115 - val_loss: 0.2536 - val_binary_accuracy: 0.9527\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2913 - binary_accuracy: 0.9115 - val_loss: 0.2414 - val_binary_accuracy: 0.9519\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2859 - binary_accuracy: 0.9121 - val_loss: 0.2369 - val_binary_accuracy: 0.9536\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 5s 19ms/step - loss: 0.2782 - binary_accuracy: 0.9146 - val_loss: 0.2356 - val_binary_accuracy: 0.9534\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 3s 11ms/step - loss: 0.2741 - binary_accuracy: 0.9142 - val_loss: 0.2278 - val_binary_accuracy: 0.9532\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2684 - binary_accuracy: 0.9149 - val_loss: 0.2236 - val_binary_accuracy: 0.9534\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 4s 14ms/step - loss: 0.2644 - binary_accuracy: 0.9165 - val_loss: 0.2214 - val_binary_accuracy: 0.9536\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2628 - binary_accuracy: 0.9147 - val_loss: 0.2153 - val_binary_accuracy: 0.9533\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.2599 - binary_accuracy: 0.9162 - val_loss: 0.2122 - val_binary_accuracy: 0.9533\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2536 - binary_accuracy: 0.9163 - val_loss: 0.2112 - val_binary_accuracy: 0.9537\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2572 - binary_accuracy: 0.9148 - val_loss: 0.2125 - val_binary_accuracy: 0.9544\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2528 - binary_accuracy: 0.9148 - val_loss: 0.2107 - val_binary_accuracy: 0.9554\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2485 - binary_accuracy: 0.9152 - val_loss: 0.2079 - val_binary_accuracy: 0.9550\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2511 - binary_accuracy: 0.9136 - val_loss: 0.2075 - val_binary_accuracy: 0.9544\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2430 - binary_accuracy: 0.9183 - val_loss: 0.2031 - val_binary_accuracy: 0.9549\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2411 - binary_accuracy: 0.9185 - val_loss: 0.2015 - val_binary_accuracy: 0.9553\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2411 - binary_accuracy: 0.9173 - val_loss: 0.2031 - val_binary_accuracy: 0.9559\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2365 - binary_accuracy: 0.9196 - val_loss: 0.2027 - val_binary_accuracy: 0.9549\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2406 - binary_accuracy: 0.9154 - val_loss: 0.2000 - val_binary_accuracy: 0.9550\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2358 - binary_accuracy: 0.9185 - val_loss: 0.1999 - val_binary_accuracy: 0.9556\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2334 - binary_accuracy: 0.9178 - val_loss: 0.1989 - val_binary_accuracy: 0.9560\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2331 - binary_accuracy: 0.9183 - val_loss: 0.2006 - val_binary_accuracy: 0.9566\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2311 - binary_accuracy: 0.9163 - val_loss: 0.1998 - val_binary_accuracy: 0.9566\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2279 - binary_accuracy: 0.9191 - val_loss: 0.1968 - val_binary_accuracy: 0.9561\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2307 - binary_accuracy: 0.9168 - val_loss: 0.1962 - val_binary_accuracy: 0.9571\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.2279 - binary_accuracy: 0.9182 - val_loss: 0.1959 - val_binary_accuracy: 0.9571\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.2269 - binary_accuracy: 0.9185 - val_loss: 0.1969 - val_binary_accuracy: 0.9564\n",
      "93/93 [==============================] - 1s 7ms/step\n",
      "99/99 [==============================] - 0s 3ms/step\n",
      "87/87 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 24ms/step - loss: 0.5726 - binary_accuracy: 0.7297 - val_loss: 0.7016 - val_binary_accuracy: 0.7286\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.4730 - binary_accuracy: 0.9082 - val_loss: 0.6482 - val_binary_accuracy: 0.8571\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.4109 - binary_accuracy: 0.9580 - val_loss: 0.5671 - val_binary_accuracy: 0.8636\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3703 - binary_accuracy: 0.9659 - val_loss: 0.5331 - val_binary_accuracy: 0.8583\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.3363 - binary_accuracy: 0.9677 - val_loss: 0.5131 - val_binary_accuracy: 0.8674\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.3084 - binary_accuracy: 0.9726 - val_loss: 0.4916 - val_binary_accuracy: 0.8790\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.2867 - binary_accuracy: 0.9723 - val_loss: 0.4611 - val_binary_accuracy: 0.8886\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.2702 - binary_accuracy: 0.9742 - val_loss: 0.4608 - val_binary_accuracy: 0.8999\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 5s 18ms/step - loss: 0.2546 - binary_accuracy: 0.9735 - val_loss: 0.4465 - val_binary_accuracy: 0.8928\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 3s 12ms/step - loss: 0.2412 - binary_accuracy: 0.9744 - val_loss: 0.4263 - val_binary_accuracy: 0.9022\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.2283 - binary_accuracy: 0.9751 - val_loss: 0.4144 - val_binary_accuracy: 0.9045\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 7s 23ms/step - loss: 0.2167 - binary_accuracy: 0.9755 - val_loss: 0.4134 - val_binary_accuracy: 0.9095\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 5s 20ms/step - loss: 0.2056 - binary_accuracy: 0.9761 - val_loss: 0.4002 - val_binary_accuracy: 0.9107\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 6s 22ms/step - loss: 0.1983 - binary_accuracy: 0.9761 - val_loss: 0.4037 - val_binary_accuracy: 0.9076\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1934 - binary_accuracy: 0.9762 - val_loss: 0.4102 - val_binary_accuracy: 0.8934\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1860 - binary_accuracy: 0.9761 - val_loss: 0.3817 - val_binary_accuracy: 0.9126\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.1802 - binary_accuracy: 0.9764 - val_loss: 0.3966 - val_binary_accuracy: 0.9048\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.1745 - binary_accuracy: 0.9764 - val_loss: 0.3782 - val_binary_accuracy: 0.9106\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1705 - binary_accuracy: 0.9765 - val_loss: 0.3853 - val_binary_accuracy: 0.9048\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 7s 23ms/step - loss: 0.1631 - binary_accuracy: 0.9766 - val_loss: 0.3682 - val_binary_accuracy: 0.9144\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1611 - binary_accuracy: 0.9778 - val_loss: 0.3687 - val_binary_accuracy: 0.9138\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 6s 21ms/step - loss: 0.1588 - binary_accuracy: 0.9766 - val_loss: 0.3704 - val_binary_accuracy: 0.9109\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 9s 32ms/step - loss: 0.1569 - binary_accuracy: 0.9764 - val_loss: 0.3693 - val_binary_accuracy: 0.9094\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 8s 29ms/step - loss: 0.1500 - binary_accuracy: 0.9782 - val_loss: 0.3679 - val_binary_accuracy: 0.9092\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 8s 29ms/step - loss: 0.1531 - binary_accuracy: 0.9759 - val_loss: 0.3710 - val_binary_accuracy: 0.9084\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.1458 - binary_accuracy: 0.9776 - val_loss: 0.3657 - val_binary_accuracy: 0.9107\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.1411 - binary_accuracy: 0.9781 - val_loss: 0.3674 - val_binary_accuracy: 0.9102\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 7s 27ms/step - loss: 0.1449 - binary_accuracy: 0.9768 - val_loss: 0.3699 - val_binary_accuracy: 0.9079\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 8s 28ms/step - loss: 0.1411 - binary_accuracy: 0.9776 - val_loss: 0.3544 - val_binary_accuracy: 0.9137\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 7s 26ms/step - loss: 0.1424 - binary_accuracy: 0.9754 - val_loss: 0.3561 - val_binary_accuracy: 0.9127\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 8s 27ms/step - loss: 0.1355 - binary_accuracy: 0.9788 - val_loss: 0.3568 - val_binary_accuracy: 0.9114\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 7s 26ms/step - loss: 0.1372 - binary_accuracy: 0.9764 - val_loss: 0.3647 - val_binary_accuracy: 0.9073\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.1366 - binary_accuracy: 0.9775 - val_loss: 0.3674 - val_binary_accuracy: 0.9056\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 6s 23ms/step - loss: 0.1339 - binary_accuracy: 0.9766 - val_loss: 0.3624 - val_binary_accuracy: 0.9079\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.1345 - binary_accuracy: 0.9768 - val_loss: 0.3485 - val_binary_accuracy: 0.9126\n",
      "93/93 [==============================] - 1s 8ms/step\n",
      "99/99 [==============================] - 1s 7ms/step\n",
      "87/87 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "279/279 [==============================] - 13s 25ms/step - loss: 0.6654 - binary_accuracy: 0.5490 - val_loss: 0.6989 - val_binary_accuracy: 0.8142\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 7s 25ms/step - loss: 0.5413 - binary_accuracy: 0.8020 - val_loss: 0.6225 - val_binary_accuracy: 0.8869\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 7s 24ms/step - loss: 0.4682 - binary_accuracy: 0.8594 - val_loss: 0.4781 - val_binary_accuracy: 0.9495\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 6s 20ms/step - loss: 0.4219 - binary_accuracy: 0.8784 - val_loss: 0.4193 - val_binary_accuracy: 0.9509\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 3s 12ms/step - loss: 0.3907 - binary_accuracy: 0.8884 - val_loss: 0.3721 - val_binary_accuracy: 0.9522\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.3623 - binary_accuracy: 0.8973 - val_loss: 0.3481 - val_binary_accuracy: 0.9515\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 3s 10ms/step - loss: 0.3403 - binary_accuracy: 0.9016 - val_loss: 0.3183 - val_binary_accuracy: 0.9534\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.3267 - binary_accuracy: 0.9025 - val_loss: 0.2967 - val_binary_accuracy: 0.9524\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.3163 - binary_accuracy: 0.9065 - val_loss: 0.2907 - val_binary_accuracy: 0.9513\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.3058 - binary_accuracy: 0.9061 - val_loss: 0.2735 - val_binary_accuracy: 0.9537\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2965 - binary_accuracy: 0.9090 - val_loss: 0.2589 - val_binary_accuracy: 0.9519\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2907 - binary_accuracy: 0.9111 - val_loss: 0.2558 - val_binary_accuracy: 0.9529\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2854 - binary_accuracy: 0.9098 - val_loss: 0.2537 - val_binary_accuracy: 0.9537\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2810 - binary_accuracy: 0.9116 - val_loss: 0.2459 - val_binary_accuracy: 0.9524\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2760 - binary_accuracy: 0.9115 - val_loss: 0.2441 - val_binary_accuracy: 0.9542\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2701 - binary_accuracy: 0.9137 - val_loss: 0.2349 - val_binary_accuracy: 0.9529\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2701 - binary_accuracy: 0.9126 - val_loss: 0.2389 - val_binary_accuracy: 0.9534\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2640 - binary_accuracy: 0.9142 - val_loss: 0.2328 - val_binary_accuracy: 0.9533\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2657 - binary_accuracy: 0.9117 - val_loss: 0.2315 - val_binary_accuracy: 0.9540\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2601 - binary_accuracy: 0.9128 - val_loss: 0.2268 - val_binary_accuracy: 0.9532\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2549 - binary_accuracy: 0.9144 - val_loss: 0.2282 - val_binary_accuracy: 0.9536\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2570 - binary_accuracy: 0.9135 - val_loss: 0.2268 - val_binary_accuracy: 0.9542\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2533 - binary_accuracy: 0.9137 - val_loss: 0.2245 - val_binary_accuracy: 0.9543\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2531 - binary_accuracy: 0.9140 - val_loss: 0.2249 - val_binary_accuracy: 0.9543\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2472 - binary_accuracy: 0.9143 - val_loss: 0.2246 - val_binary_accuracy: 0.9542\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2454 - binary_accuracy: 0.9153 - val_loss: 0.2191 - val_binary_accuracy: 0.9534\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2447 - binary_accuracy: 0.9153 - val_loss: 0.2198 - val_binary_accuracy: 0.9551\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2433 - binary_accuracy: 0.9146 - val_loss: 0.2180 - val_binary_accuracy: 0.9550\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2427 - binary_accuracy: 0.9144 - val_loss: 0.2182 - val_binary_accuracy: 0.9561\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2420 - binary_accuracy: 0.9163 - val_loss: 0.2169 - val_binary_accuracy: 0.9551\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2371 - binary_accuracy: 0.9164 - val_loss: 0.2176 - val_binary_accuracy: 0.9553\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2404 - binary_accuracy: 0.9139 - val_loss: 0.2166 - val_binary_accuracy: 0.9549\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2365 - binary_accuracy: 0.9184 - val_loss: 0.2192 - val_binary_accuracy: 0.9561\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2335 - binary_accuracy: 0.9167 - val_loss: 0.2129 - val_binary_accuracy: 0.9556\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2370 - binary_accuracy: 0.9164 - val_loss: 0.2128 - val_binary_accuracy: 0.9556\n",
      "93/93 [==============================] - 0s 2ms/step\n",
      "99/99 [==============================] - 0s 2ms/step\n",
      "87/87 [==============================] - 0s 2ms/step\n",
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "279/279 [==============================] - 4s 10ms/step - loss: 0.5355 - binary_accuracy: 0.4307 - val_loss: 0.7086 - val_binary_accuracy: 0.2749\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.4536 - binary_accuracy: 0.7752 - val_loss: 0.6491 - val_binary_accuracy: 0.8969\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.4019 - binary_accuracy: 0.9196 - val_loss: 0.5633 - val_binary_accuracy: 0.8390\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.3593 - binary_accuracy: 0.9566 - val_loss: 0.5397 - val_binary_accuracy: 0.8334\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.3305 - binary_accuracy: 0.9696 - val_loss: 0.5177 - val_binary_accuracy: 0.8493\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.3062 - binary_accuracy: 0.9722 - val_loss: 0.4783 - val_binary_accuracy: 0.8800\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2817 - binary_accuracy: 0.9740 - val_loss: 0.4624 - val_binary_accuracy: 0.8985\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2629 - binary_accuracy: 0.9749 - val_loss: 0.4391 - val_binary_accuracy: 0.9029\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2481 - binary_accuracy: 0.9746 - val_loss: 0.4237 - val_binary_accuracy: 0.9035\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2329 - binary_accuracy: 0.9766 - val_loss: 0.4086 - val_binary_accuracy: 0.9060\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2253 - binary_accuracy: 0.9761 - val_loss: 0.4149 - val_binary_accuracy: 0.9048\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2125 - binary_accuracy: 0.9766 - val_loss: 0.3916 - val_binary_accuracy: 0.9085\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2064 - binary_accuracy: 0.9770 - val_loss: 0.3910 - val_binary_accuracy: 0.9098\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.1954 - binary_accuracy: 0.9773 - val_loss: 0.3925 - val_binary_accuracy: 0.9083\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.1935 - binary_accuracy: 0.9759 - val_loss: 0.3879 - val_binary_accuracy: 0.9079\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1854 - binary_accuracy: 0.9778 - val_loss: 0.3779 - val_binary_accuracy: 0.9088\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1797 - binary_accuracy: 0.9772 - val_loss: 0.3687 - val_binary_accuracy: 0.9104\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1750 - binary_accuracy: 0.9773 - val_loss: 0.3727 - val_binary_accuracy: 0.9096\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1719 - binary_accuracy: 0.9774 - val_loss: 0.3697 - val_binary_accuracy: 0.9091\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1705 - binary_accuracy: 0.9759 - val_loss: 0.3786 - val_binary_accuracy: 0.9061\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1634 - binary_accuracy: 0.9773 - val_loss: 0.3613 - val_binary_accuracy: 0.9103\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1633 - binary_accuracy: 0.9775 - val_loss: 0.3614 - val_binary_accuracy: 0.9096\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.1582 - binary_accuracy: 0.9770 - val_loss: 0.3678 - val_binary_accuracy: 0.9083\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.1548 - binary_accuracy: 0.9782 - val_loss: 0.3671 - val_binary_accuracy: 0.9091\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1517 - binary_accuracy: 0.9778 - val_loss: 0.3631 - val_binary_accuracy: 0.9088\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1491 - binary_accuracy: 0.9781 - val_loss: 0.3483 - val_binary_accuracy: 0.9104\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1512 - binary_accuracy: 0.9772 - val_loss: 0.3677 - val_binary_accuracy: 0.9065\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1486 - binary_accuracy: 0.9771 - val_loss: 0.3501 - val_binary_accuracy: 0.9102\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1474 - binary_accuracy: 0.9765 - val_loss: 0.3515 - val_binary_accuracy: 0.9095\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1416 - binary_accuracy: 0.9782 - val_loss: 0.3559 - val_binary_accuracy: 0.9081\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1418 - binary_accuracy: 0.9780 - val_loss: 0.3601 - val_binary_accuracy: 0.9072\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1425 - binary_accuracy: 0.9770 - val_loss: 0.3590 - val_binary_accuracy: 0.9061\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1428 - binary_accuracy: 0.9752 - val_loss: 0.3584 - val_binary_accuracy: 0.9065\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1340 - binary_accuracy: 0.9776 - val_loss: 0.3522 - val_binary_accuracy: 0.9084\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.1401 - binary_accuracy: 0.9763 - val_loss: 0.3495 - val_binary_accuracy: 0.9079\n",
      "93/93 [==============================] - 0s 2ms/step\n",
      "99/99 [==============================] - 0s 2ms/step\n",
      "87/87 [==============================] - 0s 2ms/step\n",
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotNormal is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "279/279 [==============================] - 4s 9ms/step - loss: 0.8222 - binary_accuracy: 0.6458 - val_loss: 0.6973 - val_binary_accuracy: 0.8095\n",
      "Epoch 2/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.6248 - binary_accuracy: 0.8122 - val_loss: 0.6097 - val_binary_accuracy: 0.8663\n",
      "Epoch 3/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.5215 - binary_accuracy: 0.8563 - val_loss: 0.4812 - val_binary_accuracy: 0.9417\n",
      "Epoch 4/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.4542 - binary_accuracy: 0.8752 - val_loss: 0.4233 - val_binary_accuracy: 0.9466\n",
      "Epoch 5/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.4126 - binary_accuracy: 0.8847 - val_loss: 0.3886 - val_binary_accuracy: 0.9513\n",
      "Epoch 6/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.3792 - binary_accuracy: 0.8950 - val_loss: 0.3633 - val_binary_accuracy: 0.9505\n",
      "Epoch 7/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.3544 - binary_accuracy: 0.9028 - val_loss: 0.3324 - val_binary_accuracy: 0.9502\n",
      "Epoch 8/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.3383 - binary_accuracy: 0.9025 - val_loss: 0.3272 - val_binary_accuracy: 0.9513\n",
      "Epoch 9/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.3218 - binary_accuracy: 0.9089 - val_loss: 0.3153 - val_binary_accuracy: 0.9520\n",
      "Epoch 10/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.3133 - binary_accuracy: 0.9065 - val_loss: 0.3097 - val_binary_accuracy: 0.9520\n",
      "Epoch 11/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.3018 - binary_accuracy: 0.9106 - val_loss: 0.3035 - val_binary_accuracy: 0.9527\n",
      "Epoch 12/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2940 - binary_accuracy: 0.9096 - val_loss: 0.2967 - val_binary_accuracy: 0.9519\n",
      "Epoch 13/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2882 - binary_accuracy: 0.9124 - val_loss: 0.2998 - val_binary_accuracy: 0.9534\n",
      "Epoch 14/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2830 - binary_accuracy: 0.9100 - val_loss: 0.2987 - val_binary_accuracy: 0.9534\n",
      "Epoch 15/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2737 - binary_accuracy: 0.9134 - val_loss: 0.2944 - val_binary_accuracy: 0.9536\n",
      "Epoch 16/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2765 - binary_accuracy: 0.9136 - val_loss: 0.2968 - val_binary_accuracy: 0.9546\n",
      "Epoch 17/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2646 - binary_accuracy: 0.9153 - val_loss: 0.2936 - val_binary_accuracy: 0.9546\n",
      "Epoch 18/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2651 - binary_accuracy: 0.9161 - val_loss: 0.2934 - val_binary_accuracy: 0.9546\n",
      "Epoch 19/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2634 - binary_accuracy: 0.9158 - val_loss: 0.2937 - val_binary_accuracy: 0.9573\n",
      "Epoch 20/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2612 - binary_accuracy: 0.9137 - val_loss: 0.2912 - val_binary_accuracy: 0.9557\n",
      "Epoch 21/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2591 - binary_accuracy: 0.9142 - val_loss: 0.2891 - val_binary_accuracy: 0.9563\n",
      "Epoch 22/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2559 - binary_accuracy: 0.9143 - val_loss: 0.2846 - val_binary_accuracy: 0.9547\n",
      "Epoch 23/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2497 - binary_accuracy: 0.9153 - val_loss: 0.2865 - val_binary_accuracy: 0.9567\n",
      "Epoch 24/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2529 - binary_accuracy: 0.9179 - val_loss: 0.2858 - val_binary_accuracy: 0.9567\n",
      "Epoch 25/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2508 - binary_accuracy: 0.9145 - val_loss: 0.2829 - val_binary_accuracy: 0.9566\n",
      "Epoch 26/35\n",
      "279/279 [==============================] - 2s 8ms/step - loss: 0.2461 - binary_accuracy: 0.9167 - val_loss: 0.2828 - val_binary_accuracy: 0.9567\n",
      "Epoch 27/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2454 - binary_accuracy: 0.9161 - val_loss: 0.2822 - val_binary_accuracy: 0.9564\n",
      "Epoch 28/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2427 - binary_accuracy: 0.9137 - val_loss: 0.2802 - val_binary_accuracy: 0.9570\n",
      "Epoch 29/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2400 - binary_accuracy: 0.9170 - val_loss: 0.2828 - val_binary_accuracy: 0.9586\n",
      "Epoch 30/35\n",
      "279/279 [==============================] - 3s 9ms/step - loss: 0.2389 - binary_accuracy: 0.9157 - val_loss: 0.2785 - val_binary_accuracy: 0.9568\n",
      "Epoch 31/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2384 - binary_accuracy: 0.9166 - val_loss: 0.2791 - val_binary_accuracy: 0.9580\n",
      "Epoch 32/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2346 - binary_accuracy: 0.9149 - val_loss: 0.2761 - val_binary_accuracy: 0.9577\n",
      "Epoch 33/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2359 - binary_accuracy: 0.9151 - val_loss: 0.2742 - val_binary_accuracy: 0.9580\n",
      "Epoch 34/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2342 - binary_accuracy: 0.9152 - val_loss: 0.2742 - val_binary_accuracy: 0.9580\n",
      "Epoch 35/35\n",
      "279/279 [==============================] - 2s 9ms/step - loss: 0.2348 - binary_accuracy: 0.9155 - val_loss: 0.2748 - val_binary_accuracy: 0.9588\n",
      "93/93 [==============================] - 0s 2ms/step\n",
      "99/99 [==============================] - 0s 2ms/step\n",
      "87/87 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oycWUMtQGJk+Y8gpR9UOWCdZ2ycN+SgU0sNUv5TgUntKu+qHdKxDaodkT5Gt/AOl99qXnutd93vebQOkjTNcpmfEO17U/YBqUsjRS8uIpAYry+vFU//nt16y3H7pN7fezp8TFXK+/TurdJ75/hB3oPUXrMYr1Ola6xOz/+JnPXBN/cNqlfw4j3DQ31ZUiO39q+EtSv0wrikWKtrpvl7inPxjYPUNk92bPZsUlpXLfE8/LgsX/kW15BVJd7ZKy8grJyc2t+8tX//N4fVCP69NbJM49MF9KWpKktUiVvN3FIrGJ+0pN3P+46X84/Y/SqrtIaqumfRnrF4Rd0rC/YtD66+z9bfClUPvFUH+dXjbpi6qRdd5fLvv9Mqpdd8Av8PqlMDH7vpAaK+2pU6pQs68Kos6Xv9eXmPmiqveFprft93NQvEq7vE+Y9hd1YydRz3GvVzzvqzrAXqf7GZfo/pzpF2xsUu1lotd6r0XX6WPql8LUue3jZK/3e5eC1a/Kaax0qk7J3IFCRO06c4JpSmmcj9dm/CigSQg3Taga0hIUp167Dv1y3LXJ/QWpX+SJafuqRPQL3axrpRvuCyItM0TitOeTe7s7HnpUPvr0C3n8wXulR/fukpScIhdccY1URqeIZPSsPQnGi6Rli2Qe63npuDadRdr29NyOio6RmtQskXbH+N758nKRvbEil80TSdQABISrBKd3AEA9hJsD0DYvPquGgk0DzJ5cd1sDDSCtu7l/wdaKT24hLg0xrbu4V6TnuC/bdNcpuj3bfb5yjVx51TVy7uXjze3i4mL5OSdXJDZenyTIbwoAAP9jEL/mojhPpHy3u2i/Vdc6wcbu4bR8+XL5+eefpaCgQGq0qsKHI488UubPny+rV6+Wb775Ri699NJGtwUAoDki3DQH5YUie391X0/PFklIbbDJHXfcYWbf7tWrlxmnJientuSmnhkzZpheU0OHDjW9pEaMGCH9+7sbCgMAEA6iLO3rHEGKiookPT1dCgsLJS0trc595eXlsnnzZunataskhko7Ee0Zpb2WtMFjchuRlp2kOQjJYwkACMvzd32U3IQybTisDYg12MSniKQf4fQeAQAQ8gg3oUoL1HZvcY/JYbpUd63ttgsAAPaHs2Wo2rtNpKLI3YBYe0Z5RuIFAAD7Q7gJRaW7RIp3uK9rGxu6aAMA0GSEm1BTWSqyp7anU2o7kWSduwkAADQV4SaU6FQC2oBYRxNOSBNp4dx08QAANFchMPQuDJ1/RueA0jmCYhJEWnVmHhkggMqrXLK9sFy2FZZLQXGFme4kPTlO0pP2LYn1p0AJUzoiSFF5tWwrLJPY6CjJSE0w719HaPeXyuoayS+uMMc8r6hcisqrpE1KgmSlJ5qldXK8REeH73eeq8aSveVVsre8WgrLqsz71+vF5dUSHxstqQmxkpoY6760l8RYiYuJ9svft8pliR7eWD88X3NAuAkVhb+IVJa4J8nTBsQOTxcPhENw+bWwzBNg9MRt1u0pl+1F5bKr5MAzdyfERtcJO/aSVnupJ6DE+BgTjHRJjIt2X3qtS4rX9e7rcTFRfg0MBxMsdhSVy9Y9ZfKrvZhjYd8ul+KKupPxashpkxpvgk6b1ATJSI2XtuYywbPevcRLTHSUOaZ5RRXmcodnqQ0zezVA7v9467Fp18IddLLSEiUzTa8nuC/Ndfc6/Zsc7jG0T/aVrhpzbDyLyyUVdW7XSJW9jW5fu37furq39bEaYDQoFpW5w4snxNQ7vk2VUC/4pCTESouEWHMM3K/vqrMv9n5U2tdr1yv9O3VpkyxHZbaQIzNbSM/MFnJUZqp0yUjxS4gKJZxBQ0FJvkjpTvd1LbHRmY2BCKUnnvKqGimtrJaSCpfn5GBf2r9+zUmkrFr2Vtj3u9ftLqmU3aVeM9zvh4aRDulJktEiwQQi/UVtflWXVUmNJeYkkbe3wiz+oCeX+Jhoc6m/ovUyJlpvi8RERUlMTJS51BIMDRfRus5ss++6Pk5PbO7txKz3dV+NZZn91lCnl00ZrrVVcpwpYdBjWV1jmXCii7/YASYzLcEERC0x215YITtLKkzY0PCly4FotrGPk7nUieRrj5O93hzf2us1Ne4go3/Pitow4tTwtRpyWyTGmveflugOK7o/Gn708+4OQlXm/4By73Ol7GxCGD8QV40lG/NLzPKvNdvr/F26ZqSYwHNUO3fg0esahPZX0qP/V/U5PcHKK2BpWOrY2rnOMIQbp1UUixRudV9v0V4kMd2R3dC5qW699VazwDn6JaclCiUV1ebLraLa1eCyoqpGyu3LKvcvTfvS3K+X9ra1X+b24+pvY4llqgNap8ZL65QEaZ0cZy71l3nrFPfSJiVeWtVepiXGeaoO9IRRVuUy+1pS6b4srb0sqayW0gqX+cI2IaXSJaUV1WZ73aZMb+tSpdfdjyu376ty+eXEYweX9i31l3+SdNDL9ERpb5Ykc9lY1Yu+t+LKaiks3Rd2Cn0sZbX7a5ba97Dvtvvvou9fg5LSE0FZjUucoFUf2S3dx0GPSwf7urlMMuu0lEnpZ2RncaUJH3qp1Un2db00y1494WowqTR/Ly3B0ZKVfcu+Upd2aQnmslUjVU/6udcApqU8WuLjuax33T7h6+tV6z/2gfVT6NRjZJaYaFNiYt/WE7Wui/O6TwNBnftjoyUhJlpaJGrJXqz5v1L3eqy5rds1RbWrxh12Kqpqg0+1pwRIq7L0rdfZ3zj363vWxbrX29cTYmPMZ/HHHcXy04698qNZ3Nf1/6de12WBbNv3mTEBJcldSuRdYmUHmf2ExOO7tJK3rhsqTiHcOM30jLJEEluJpGY6vTcIED3J5ZsSAHfRfV6D6xWSv7fcc6IIJlM9UVje5JOA/trUcKRBJBi/cvXk0MLr5GB+9drXE9y/gL3v08Cyv+DSFHoC1hOSLh0P8z3YVSAaeEwIraoRV+0vXi1dqXa5L/W2lpbY1z2L3nZpDHWHI30+PbHpevd13U7P8+7bnusiphrJDjQaVJt6PPREaIeeA7Hfx+FUa+hjdT91aYy+t70V1VJV7T5+Ot+v+3LfcbLfv71P9notwWkYBGI86/RzHWq0xCQ9WRf/jXGWbv5vJMnJR7X1rNNjpv//Nexo0Fm/vVh+ytPrxeYzq6U8TaUfLztQOd1ejXDjJD2L6QjEKq0DDYiDQE8uv+wuNb8A9deXfom7f9XYv26iD9jgTr9M9Vf77lJ39Ye7GsReqmRPaaUpfdHreuluPNn0+nb9ok2ubaeh+9PUS7PY173Xxca4v8xrr2uJhlkXG20+grvM/rp/let70IC1q9j9Hsx1fS8llebEoieL+lU++rFNidfi9RhzmZwQI8nx7vYB+j7sdXqpJQO6zm6LotuZ27Xrk+PqbhMODUw1UMTHuk+uenIJN6YqSAcbDcJx1LAJ/x/X7Npg+T8929X5ntMqwpxdpaba03yGawOhd6mVp3SotrrViTZlIRluZs2aJY899phs375d+vbtK08//bQMGjTI57ZVVVUyffp0+etf/ypbt26Vnj17yiOPPCJnnnmmNEveP9G18vwQ/eUvf5H77rtPfvnlF4n2ep5zzjlH2rRpI/fcc49MnDhRvvzySykpKZFjjjnGHMfhw4dLuNFfIXoy3rKrVHJ3lcqWnaXmP2dO7aUWbR+Ink/tQGD/ytNLMyNGaaUJNodSGq5fAO1aJNQu7qJ6c12L7b3WBbvXSKc2TasX16qK3SXu6hgNSHaA0euh8oUGwD/0O0jbzDjZbqbZhpt58+aZk+7s2bNl8ODBMnPmTBkxYoSsX79e2rXblyBt9957r7z22mvywgsvyNFHHy0ffPCBnHvuufLFF1/Ib37zm8DspJ7RqkoD89yuapGqsn2zf9c/QcQlN6k058ILL5SbbrpJlixZIqeddppZt2vXLlm0aJEsXLhQiouL5ayzzpKHHnpIEhIS5JVXXpGRI0ea49ypU+jOMq51zu42Gfvac9htPMy62vX662LLzhLJ2VVmAs2BeiW4exzE1OlVoNUBNr1qt5s40PO0Sokz7QhaaruV5Dhzqbdbp7iva1VA2xYJktki0VSvNOcQoIEvK10XGrwDCG1Rlv7UdYgGmuOPP16eeeYZc7umpkY6duxoTtSTJk1qsH2HDh1MKcSNN97oWXf++edLUlKSCT2HO2V6eXm5bN68Wbp27SqJibVf4No9+08ODaZ396/u2cCbYNSoUaaU5sUXX/SU5tx///2Sm5tbpzTH1rt3b7nuuutkwoQJAWlQ7PNYetGqm435xbIhr9jdej+vWHJ3l5oGdHYDVLv74qHQNhf6i6Nz62TppEubZOncJsVc1x4h9UOGBim7pb8deOxGvKaXRVWNyZkaXDTQtEyKb3LDQADA4dvf+TtkSm4qKytl5cqVMnnyZM86PQlrVcmyZct8PqaioqLBiVKDzWeffdbo6+hjdPE+OOHosssuk/Hjx8uzzz5rSmdef/11ufjii80x1ZIbrbZasGCBbNu2Taqrq6WsrExycmqneQgQjc3b9pTJz4VFJrxomNmYp90Qiw+qW6PW0KR4tdew22kka5uOuBjTG0ZDS+c27iBzRKvkg27Mpu1sdEmOP/j3CQAILY6Fm4KCAnG5XJKZWbeHkN5et26dz8doldWMGTPkpJNOku7du8vixYtl/vz55nkao21LtATjkGnVkJagBGoeqZ0/iUTHiWT28v3aTaTVTFoIpwFGS8P+85//yJ///Gdz3x133CEfffSRPP7449KjRw8TCC+44AITMP1Fuwja3WC1tKOkVAcJK5Np7yyXrXt9/306pCdK93ap0r1tqrnUMRW0waDdwDSlNtD4Y9AuAEDkcLxB8cF48sknTemEtrfRk50GnHHjxsmcOXMafYyWDGm7Hu+SG636Ori+bU2rGjqkoo24JPd0C4f5Glqidd5555kSmw0bNpjG1v379zf3ff7553LllVea9klKS3J+/vnnQ9xlyzOuih1ktP1LtfbL9N6u2j1WSVx0tPTMTJbu7VJMiOlRG2Z0wCgdvAoAAH9z7OySkZEhMTExsmPHjjrr9XZWVpbPx7Rt21beffdd055j586dpg2Ots3p1q1bo6+jVTS6hOx8Uioq2m9VU7///e9l7dq1cvnll3vWH3nkkaaES0t3NBROmTLFtG864O5ZlnuwNQ0x2pi3diA4HT/CF7ubsenCWxMj0cUJsuCWYZKSfOCxMgAAaPbhJj4+XgYMGGCqlrQxrNITrt62G7nur5QiOzvbdA3/+9//LhdddJE0S5bLr+Hm1FNPldatW5teUJdeeqlnvVblXXXVVTJ06FATKu+6665G2x5pcNEeSO6J3apNQ9v6dMwD91w57jFWNMzouCreA2GVl7vbsYTi4FgAgPDmaL2AVheNHTtWBg4caMa20a7gOg6LVjWpMWPGmBCj7WbU8uXLzfg2/fr1M5faSFYD0R/+8AdpluySm8MY48abNh7+9deG7YO0J9Qnn3xSZ513jzMdmG319z+aeXp++LXIjOhp05E9tfrIO8xoLyHawAAAQpWj4Wb06NGSn58vU6dONYP4aWjRsVnsRsbam8e7G7NWR+lYN5s2bZLU1FQzdsurr74qLVu2lGbJz9VSB0NLZDwz11ZUmyoom5a46PD2OpqqBhstqQEAoLlwdJwbJxz0ODeBVLxDpOhXkaRWIq26BPzldOwWnVlZq5x0EkPvP7w9NLzdW+lwS2aCfiwBAGGtqDmMc4PgldxUVrtkR1GFmTqg4aSEOmttnCRS1QQACBOEmzAON1r1pLNNu2eadpfTpMS7Z1FOT4qV+FhnZ20FACAQCDc+BK2mriYw4UZncy0oqZD8vRWmsbA9D5LOCaSj+wZDhNV2AgBCCOHGS1xcnLksLS01o/g2t5IbDRS7S6tkR1G5GTFYae8mnWdJw00wq530GHofUwAAgoVw40UHFdSeV3l5eeZ2cnJyYANBRZVItSVS6XIPDHMYoUbHpskvrjTta1RsdLRkpMa7Z6IWl1RU7H+Ga3/RfdFgo8dQj6UeUwAAgolwU489OrIdcAKqJF+kqkwk2SUSX3TIPaC095NOiaB0zLwWibESlxAru0qiZJc4Q4NNYyNNAwAQSISberSkpn379tKuXTszAnJAvfOIyNavRc54SKTriIN66NbdpTLn882ydH2+uR0XEy3n/iZbLhnUUdKSnJ3aWquiKLEBADiFcNMIPTkH/ARdnCNSnCsSH6dzSjT5YXNX5MiUf6yRKpdl5vXUUHP7GT0luyVzOAEAQLhxUqW70a3EJzf5IT8XlMi099aaYDPsyAyZ/LtjpFeH/Q9mBABAJCHcOKmqxH0Zl9Lkxrp3v/OdaV+jweaVqwYx8B4AAPUEf1IjNCy5iWtaddJbK3+RLzbulMS4aHloVB+CDQAAPhBunKQ9pZpYLaUD8j204Adz/bbhR0mnNk2vygIAIJIQbpyiI/hWlTa5WuqBf35vunwf2yFNrj6xa+D3DwCAZopw42ipjdWkaqkl6/Lk/W9+NWPYPHzecRIbw58NAIDGcJZ0ukpKxTdecqMjD9/77hpzXUts+hyRHoy9AwCg2SLcON1TKiZBJLrx8XSe+PBH2bqnTI5olSS3nX5U8PYPAIBminATwj2lvsndIy9/sdlcf+jcPkGb0RsAgOaMcOMUuzFxI1VSOqv3XX//VmoskVH9OsjJR7UN7v4BANBMEW6c4ukp5btL9wv/2STrtu+VVslxMuX3vYK7bwAANGOEmxCcekGnWHjy45/M9XvP7iVtUhOCvXcAADRbhBvHp15IbnSKhRN7ZMh5/bOd2T8AAJopwo3TXcHrhZu3vadYOLc3UywAAHCQCDdOqSxpUC1VUFwhDy10T7Fw6/CjpHObpk2oCQAA9iHchFCD4gfe/172lFZJr/Zpcg1TLAAAcEgINyFSLbVkfZ68VzvFwiPnM8UCAACHijOo49VSKe4pFt5xT7Fw1QlMsQAAwOEg3IRAtdSMj9xTLGS3TJKJZzDFAgAAh4Nw4/A4N9tKo+Slz+0pFnozxQIAAIeJcONwyc376wrNFAvn9Osgp/Rs5/ReAQDQ7BFuHA43P+2uMZd3n3WMwzsEAEB4INw4XC1VbsVLm5R4yUxLdHqPAAAIC4Qbh0tuSiVBurVlsD4AAPyFcON4uEmUbhmpTu8NAABhw/FwM2vWLOnSpYskJibK4MGDZcWKFfvdfubMmdKzZ09JSkqSjh07ym233Sbl5eXSXKulyixKbgAACJtwM2/ePJk4caJMmzZNVq1aJX379pURI0ZIXl6ez+3/9re/yaRJk8z2P/zwg7z44ovmOe6++25priU3ZRIv3dpScgMAQFiEmxkzZsj48eNl3Lhx0qtXL5k9e7YkJyfLnDlzfG7/xRdfyAknnCCXXnqpKe0544wz5JJLLjlgaU8osmhzAwBAeIWbyspKWblypQwfPnzfzkRHm9vLli3z+ZihQ4eax9hhZtOmTbJw4UI566yzGn2diooKKSoqqrM4rqZGomrDTUVUonRstW/yTAAAcHgcGw63oKBAXC6XZGZm1lmvt9etW+fzMVpio4878cQTxbIsqa6uluuuu26/1VLTp0+X+++/X0JKde2kmSKS0aqlxMc63vQJAICw0azOqkuXLpU//elP8uyzz5o2OvPnz5cFCxbIgw8+2OhjJk+eLIWFhZ4lNzdXQmZGcBHpkNHa0V0BACDcOFZyk5GRITExMbJjx4466/V2VlaWz8dMmTJFrrjiCrnmmmvM7T59+khJSYlce+21cs8995hqrfoSEhLMEoozgpdbcdK1XZrTewMAQFhxrOQmPj5eBgwYIIsXL/asq6mpMbeHDBni8zGlpaUNAowGJKXVVM2vp5Q2JqanFAAA/uToFNTaDXzs2LEycOBAGTRokBnDRktitPeUGjNmjGRnZ5t2M2rkyJGmh9VvfvMbMybOhg0bTGmOrrdDTrPg1VOqawY9pQAACJtwM3r0aMnPz5epU6fK9u3bpV+/frJo0SJPI+OcnJw6JTX33nuvREVFmcutW7dK27ZtTbB56KGHpDmpLCuWeAbwAwAgIKKsZlWfc/i0K3h6erppXJyW5kx7l60r3pXshWNlrdVNet23ygQ2AADgn/N3s+otFS7yd+5yX4lLItgAAOBnhBsH7Nyzx1xGJ1AlBQCAvxFuHKBFaioukZ5SAAD4G+HGAXv3usNNYgrhBgAAfyPcOKCspNhcJqcwgB8AAP5GuAmyXSWVElXtHuemRVq607sDAEDYIdwE2ab8YkmWCnOdNjcAAPgf4SbINuWXSFJUpftGXJLTuwMAQNgh3ATZxoJiSZJy9404uoIDAOBvhJsg25xf4qmWkvhkp3cHAICwQ7gJsk0FJZIcVRtuqJYCAMDvCDdBVO2qkS07SyRR7DY3VEsBAOBvhJsg+mV3mVS5LEmxS26olgIAwO8IN0G0qcA9eF+LaLvkhnADAIC/EW6C3A1cJRNuAAAIGMJNkBsTq0Srtis41VIAAPgd4SbIoxNHSY3E1di9pWhQDACAvxFuglwt5ekppegKDgCA3xFugmRveZXk7a3YN4Cfos0NAAB+R7gJks217W2yU2rcK2KTRKI5/AAA+Btn1yCHmyNbxrhXUCUFAEBAEG6CZGNtN/CuLaPcK+JpTAwAQCAQboLYU0p1TqtdQXsbAAACgnAT5AH8OtoFNoxxAwBAQBBugqCmxvK0uclKrm1QTMkNAAABQbgJgh17y6WsyiWx0VGSkeByryTcAAAQEISbIFZJdWqdLLHVZe6VVEsBABAQhJsgNibu1jZFpModdCi5AQAgMAg3QewG3q1tqkhVbckN4QYAgIAg3ARxNvBuGSkilbUlN1RLAQAQEISbINhc4K6W6qrhpqrUvZKSGwAAAoJwE2DlVS75Zbe7KopqKQAAIiTczJo1S7p06SKJiYkyePBgWbFiRaPbnnLKKRIVFdVgOfvssyUUbdlZKpYl0iIxVjJS472qpZh+AQCAsAw38+bNk4kTJ8q0adNk1apV0rdvXxkxYoTk5eX53H7+/Pmybds2z7JmzRqJiYmRCy+8UEK7p1SqCWFUSwEAEObhZsaMGTJ+/HgZN26c9OrVS2bPni3JyckyZ84cn9u3bt1asrKyPMtHH31ktg/ZcFPbmLi7trdRnmopZgUHACDswk1lZaWsXLlShg8fvm+HoqPN7WXLljXpOV588UW5+OKLJSUlNKt5NuZ7NSZWVEsBABBQseKggoICcblckpmZWWe93l63bt0BH69tc7RaSgNOYyoqKsxiKyoqkmCy55QyjYkV1VIAAIR3tdTh0FDTp08fGTRoUKPbTJ8+XdLT0z1Lx44dg7Z/lmV5pl4woxOrSsINAABhG24yMjJMY+AdO3bUWa+3tT3N/pSUlMjcuXPl6quv3u92kydPlsLCQs+Sm5srwbKrpFIKy6pE2xF7qqXskhsG8QMAIPzCTXx8vAwYMEAWL17sWVdTU2NuDxkyZL+Pfeutt0x10+WXX77f7RISEiQtLa3OEuzGxB3SkyQxLsa9kmopAADCt82N0m7gY8eOlYEDB5rqpZkzZ5pSGe09pcaMGSPZ2dmmeql+ldSoUaOkTZs20iwmzFQ1LpHqcvd1GhQDABCe4Wb06NGSn58vU6dOle3bt0u/fv1k0aJFnkbGOTk5pgeVt/Xr18tnn30mH374oTSbOaW8u4EruoIDABCe4UZNmDDBLL4sXbq0wbqePXuaxrqhbl9j4no9pVQs4QYAgEBo1r2lQl2Dail7jBttb1OvNAoAAPgHZ9gAqXbVSM6u0nolN4xODABAoBFuAiR3d5lUuSxJjIuW9mmJ9XpK0ZgYAIBAIdwEyOYCd5VUlzYpEh0dVW/qBbqBAwAQKISbADcm7m5XSdUpuaFaCgCAQCHcBMjG+tMuKKqlAAAIOMJNsHpKec8rRbUUAAABQ7gJ+AB+vqqlCDcAAAQK4SYA9pZXSf7eCnO9q89qKcINAACBQrgJgM21pTYZqQmSlhi37w6qpQAACDjCTUCnXajXcJiSGwAAAo5wE8DGxN3rhxvv6RcAAEBAEG4CYKOvxsTe0y9QLQUAQMAQboJaLUXJDQAAgUa48bOaGkt+ri256ZpRv1rKblDMIH4AAAQK4cbPtheVS1mVS2Kjo6Rj63olNMwKDgBAwBFuAlQl1alNssTFRDdSLUXJDQAAgUK48bNNtbOBN2hMrBjnBgCAgCPcBGw2cB+lM1RLAQAQcISbAM0p1aAxsaJaCgCAgCPcBGw2cKqlAABoNuFmyZIl/t+TMFBe5ZKte8p8j3FT4xJxuSfTZJwbAABCLNyceeaZ0r17d/njH/8oubm5/t+rZurnnSViWSJpibHSJiXe97xSinADAEBohZutW7fKhAkT5O2335Zu3brJiBEj5M0335TKykqJZPtGJk6VqKgo31VSEkWDYgAAQi3cZGRkyG233SarV6+W5cuXy1FHHSU33HCDdOjQQW6++Wb55ptvJBJttueU8tlTymvqhfrBBwAAhE6D4v79+8vkyZNNSU5xcbHMmTNHBgwYIMOGDZO1a9dKJNloNyb22VOKbuAAAIR0uKmqqjLVUmeddZZ07txZPvjgA3nmmWdkx44dsmHDBrPuwgsvlEitlmqAnlIAAARF7KE86KabbpI33nhDLMuSK664Qh599FHp3bu35/6UlBR5/PHHTTVVpNBjsa8bOGPcAADQrMLN999/L08//bScd955kpCQ0Gi7nEjqMr6zpFKKyqtNc5oubVIaL7mhWgoAgNALN4sXLz7wE8fGysknnyyRViWV3TJJEuNiGm5gdwWPp+QGAICQCzfTp0+XzMxMueqqq+qs18bE+fn5ctddd0mkyUpLlImnH9VwJvD64YYxbgAACL0Gxc8//7wcffTRDdYfe+yxMnv2bIlEndoky82nHSnXn9Ld9wY0KAYAIHTDzfbt26V9+/YN1rdt21a2bdvmj/0KP5TcAAAQuuGmY8eO8vnnnzdYr+sOtofUrFmzpEuXLpKYmCiDBw+WFStW7Hf7PXv2yI033mjClTZm1gEEFy5cKCGPcAMAQOi2uRk/frzceuutZqybU0891dPI+A9/+IPcfvvtTX6eefPmycSJE01VlgabmTNnmqkc1q9fL+3atWuwvU7vcPrpp5v7dIyd7Oxs2bJli7Rs2VJCHtVSAACEbri58847ZefOnWbKBXs+KS150YbEOlpxU82YMcMEpXHjxpnbGnIWLFhgGiZPmjSpwfa6fteuXfLFF19IXFycWaelPs0CJTcAAIRutZROCvnII4+YnlFffvmlmUtKQ8fUqVOb/BwailauXCnDhw/ftzPR0eb2smXLfD7mvffekyFDhphqKe2tpQMH/ulPfxKXy9Xo61RUVEhRUVGdxRGEGwAAQrfkxpaamirHH3/8IT22oKDAhBINKd709rp163w+ZtOmTfLJJ5/IZZddZtrZ6DQPWnqk1WPTpk1rtNv6/fffL46rrB2hmGopAABCM9x8/fXX8uabb0pOTo6naso2f/58CYSamhrT3uYvf/mLxMTEmAk6t27dKo899lij4UarybRdj01LbrRBtHMlNwziBwBAyFVLzZ07V4YOHSo//PCDvPPOO6bkRGcA11KV9PT0Jj2HTs+gAUUn2vSmt7Oysnw+RntIae8ofZztmGOOMV3T6wcsm/aoSktLq7M4glnBAQAI3XCj7Vz+/Oc/y/vvvy/x8fHy5JNPmqqkiy66SDp16tSk59DHacmL91QOWjKjt7VdjS8nnHCCqYrS7Ww//vijCT36fCHNUy1FyQ0AACEXbjZu3Chnn322ua6hoqSkxDQyvu2220yVUVNpddELL7wgf/3rX00p0PXXX2+ey+49NWbMmDq9r/R+bbh8yy23mFCjPas0aGkD45BHg2IAAEK3zU2rVq1k79695rqONbNmzRrp06ePGWCvtLT2JN4Eo0ePNj2utJeVVi3169dPFi1a5GlkrO15tAeVTdvKfPDBByZEHXfccea1Neg0i7msqJYCACB0w81JJ50kH330kQk0F154oQkY2t5G15122mkH9VwTJkwwiy9Lly5tsE6rrLT7ebNDtRQAAKEbbp555hkpLy831++55x4zoJ4OrHf++efLvffe6+99DA9USwEAEJrhprq6Wv75z3+aaRKUVhv5Gk0YXlzVIq7a3lyEGwAAQqtBcWxsrFx33XWekhscRKmNYhA/AABCr7fUoEGDZPXq1f7fm7APN1EisYkO7wwAAOHtkNrc6JQH2o07NzfXjFWTklK3kaz2ZEIjjYmjopzeGwAAwtohhZuLL77YXN58882edTrOjWVZ5nJ/E1lGJLqBAwAQ2uFm8+bN/t+TcEZPKQAAQjvcdO7c2f97Es4Y4wYAgNAON6+88sp+79dpE+CFaikAAEI73OiIxN50VnCddkHnmUpOTibc1Ee1FAAAod0VfPfu3XWW4uJiWb9+vZx44onyxhtv+H8vmzuqpQAACO1w48uRRx4pDz/8cINSHVByAwBAsww39ujFv/76qz+fMjwQbgAACO02N++9916d2zq+zbZt28yEmieccIK/9i18VNaGG6ZeAAAgNMPNqFGj6tzWgfvatm0rp556qjzxxBP+2rfwQckNAAChHW5qamr8vyfhjHADAEDzbHODRlAtBQBAaIeb888/Xx555JEG6x999FG58MIL/bFf4YWSGwAAQjvc/Pvf/5azzjqrwfrf/e535j7UQ7gBACC0w40O2qejEdcXFxcnRUVF/tiv8EK1FAAAoR1u+vTpI/PmzWuwfu7cudKrVy9/7Fd4qaodoTiOEYoBAAjJ3lJTpkyR8847TzZu3Gi6f6vFixebqRfeeustf+9j80fJDQAAoR1uRo4cKe+++6786U9/krfffluSkpLkuOOOk48//lhOPvlk/+9lc8es4AAAhHa4UWeffbZZ0ARUSwEAENptbr766itZvnx5g/W67uuvv/bHfoUXqqUAAAjtcHPjjTdKbm5ug/Vbt24198GLq0qkpsp9na7gAACEZrj5/vvvpX///g3W/+Y3vzH3wccYN4pwAwBAaIabhIQE2bFjR4P1OjN4bOwhN+MJ7yqpqGiR2ASn9wYAgLB3SOHmjDPOkMmTJ0thYaFn3Z49e+Tuu++W008/3Z/7F0ajE6fo9OlO7w0AAGHvkIpZHn/8cTnppJOkc+fOpipKrV69WjIzM+XVV1/19z6GSbihGzgAACEbbrKzs+Xbb7+V119/Xb755hszzs24cePkkksuMVMwwAs9pQAACKpDbiCTkpIiJ554onTq1EkqKyvNun/961/m8n//93/9t4fNHWPcAAAQ+uFm06ZNcu6558p3330nUVFRYlmWubS5XC5/7mPzxujEAACEfoPiW265Rbp27Sp5eXmSnJwsa9askU8//VQGDhwoS5cuPejnmzVrlnTp0kUSExNl8ODBsmLFika3ffnll02Q8l70cSGLaikAAEI/3CxbtkweeOABycjIkOjoaImJiTFVVNOnT5ebb775oJ5LZxefOHGiTJs2TVatWiV9+/aVESNGmODUmLS0NNPt3F62bNkiIYtqKQAAQj/caLVTixYtzHUNOL/++qu5rr2n1q9ff1DPNWPGDBk/frxpkNyrVy+ZPXu2KQ2aM2dOo4/R0pqsrCzPor20QhbVUgAAhH646d27t+klpbQa6dFHH5XPP//clOZ069atyc+jDZFXrlwpw4cP37dD0dHmtpYONaa4uNgEqY4dO8o555wja9eubXTbiooKKSoqqrMEVWVtyQ3VUgAAhG64uffee6WmpsZc10CzefNmGTZsmCxcuFCeeuqpJj9PQUGBKQWqX/Kit7dv3+7zMT179jSlOv/4xz/ktddeM/sxdOhQ+eWXX3xur1Vl6enpnkUDkWOD+AEAgNDsLaVtYmw9evSQdevWya5du6RVq1Z1ek0FwpAhQ8xi02BzzDHHyPPPPy8PPvhgg+11JGVt02PTkpugBhwaFAMAEFR+mwiqdevWB/0Yba+jjZHrz1Olt7UtTVPooIE6SvKGDRsanQdLF8d4Sm4INwAAhGy1lL/Ex8fLgAEDZPHixZ51Ws2kt71LZ/ZHq7V0vJ327dtLSCLcAAAQVI5P4a1VRmPHjjVj5AwaNEhmzpwpJSUlpveUGjNmjJnuQdvO2G18fvvb35rqMJ2s87HHHjNdwa+55hoJSVRLAQAQWeFm9OjRkp+fL1OnTjWNiPv16yeLFi3yNDLOyckxPahsu3fvNl3HdVtt46MlP1988YXpRh6SKLkBACCooiydOyGCaINi7TVVWFhoBgMMuBfPEMldLnLRqyK9mHMLAIBAn78dbXMTEaiWAgAgqAg3gcY4NwAABBXhJmjhhukXAAAIBsJN0KqlKLkBACAYCDdBmxWcNjcAAAQD4SaQXFUiNdXu61RLAQAQFISbYMwIrqiWAgAgKAg3wWhMHBUjEhPv9N4AABARCDeBVFW2r71NgGdLBwAAboSbYFRLMYAfAABBQ7gJJOaVAgAg6Ag3QSm5oTExAADBQrgJSpsbuoEDABAshJtAoloKAICgI9wEEtVSAAAEHeEmkKiWAgAg6Ag3gcS8UgAABB3hJpCYERwAgKAj3ARrhGIAABAUhJtAoloKAICgI9wEpVqKcAMAQLAQbgKJcW4AAAg6wk0gEW4AAAg6wk0gUS0FAEDQEW4CiZIbAACCjnATjHDDODcAAAQN4SYY1VJMvwAAQNAQboJSLUXJDQAAwUK4CRTL8poVnDY3AAAEC+EmUFxVIpbLfZ1qKQAAgoZwE+ipFxTVUgAABA3hJtCNiaNjRWLjnd4bAAAiRkiEm1mzZkmXLl0kMTFRBg8eLCtWrGjS4+bOnStRUVEyatQoCTnMCA4AQGSGm3nz5snEiRNl2rRpsmrVKunbt6+MGDFC8vLy9vu4n3/+We644w4ZNmyYhCRmBAcAIDLDzYwZM2T8+PEybtw46dWrl8yePVuSk5Nlzpw5jT7G5XLJZZddJvfff79069ZNQhJTLwAAEHnhprKyUlauXCnDhw/ft0PR0eb2smXLGn3cAw88IO3atZOrr75aQhZj3AAA4IhYcVBBQYEphcnMzKyzXm+vW7fO52M+++wzefHFF2X16tVNeo2Kigqz2IqKiiS44YZu4AAARFS11MHYu3evXHHFFfLCCy9IRkZGkx4zffp0SU9P9ywdO3aUoKBaCgCAyCu50YASExMjO3bsqLNeb2dlZTXYfuPGjaYh8ciRIz3rampqzGVsbKysX79eunfvXucxkydPNg2WvUtughJwPA2KqZYCACBiwk18fLwMGDBAFi9e7OnOrWFFb0+YMKHB9kcffbR89913ddbde++9pkTnySef9BlaEhISzOJcV3CqpQAAiJhwo7RUZezYsTJw4EAZNGiQzJw5U0pKSkzvKTVmzBjJzs421Us6Dk7v3r3rPL5ly5bmsv56x1EtBQBAZIab0aNHS35+vkydOlW2b98u/fr1k0WLFnkaGefk5JgeVM0O1VIAADgiyrJ0+urIoW1utGFxYWGhpKWlBe6F/nWXyPLZIsNuFzltauBeBwCACFB0EOfvZlgk0kxU2iU3tLkBACCYCDeBwiB+AAA4gnAT6N5SNCgGACCoCDcBr5Yi3AAAEEyEm4BXSxFuAAAIJsJNoDDODQAAjiDcBAolNwAAOIJwEyiEGwAAHEG4CXi1FF3BAQAIJsJNIOigz5TcAADgCMJNILgqRSyX+zojFAMAEFSEm0COcaOolgIAIKgIN4FgV0lFx4nExDm9NwAARBTCTSCnXqC9DQAAQUe4CWS1FAP4AQAQdISbQKCnFAAAjiHcBDLcUHIDAEDQEW4COYAfJTcAAAQd4SYQqJYCAMAxhJuAVksxxg0AAMFGuAlotRSjEwMAEGyEm0CgWgoAAMcQbgI6zg3VUgAABBvhJqAjFFMtBQBAsBFuAqGqtuSGaikAAIKOcBPIBsVUSwEAEHSEm0Bg4kwAABxDuAkEqqUAAHAM4Sag1VKEGwAAgo1wEwiMcwMAgGMIN4FAuAEAwDGEm0CgWgoAAMcQbgKBkhsAACI73MyaNUu6dOkiiYmJMnjwYFmxYkWj286fP18GDhwoLVu2lJSUFOnXr5+8+uqrEjIsi1nBAQCI5HAzb948mThxokybNk1WrVolffv2lREjRkheXp7P7Vu3bi333HOPLFu2TL799lsZN26cWT744AMJCdUVIlaN+zrTLwAAEHRRlqVFDc7Rkprjjz9ennnmGXO7pqZGOnbsKDfddJNMmjSpSc/Rv39/Ofvss+XBBx884LZFRUWSnp4uhYWFkpaWJn5Xukvk0a7u61N2isTE+v81AACIMEUHcf52tOSmsrJSVq5cKcOHD9+3Q9HR5raWzByI5rLFixfL+vXr5aSTTvK5TUVFhTkg3ktA2VVSMfEEGwAAHOBouCkoKBCXyyWZmZl11uvt7du3N/o4TW2pqakSHx9vSmyefvppOf30031uO336dJP07EVLhYLSU4oqKQAAIrPNzaFo0aKFrF69Wr766it56KGHTJudpUuX+tx28uTJJgzZS25ubpCmXqAxMQAATnC03iQjI0NiYmJkx44dddbr7aysrEYfp1VXPXr0MNe1t9QPP/xgSmhOOeWUBtsmJCSYJWgY4wYAgMgtudFqpQEDBph2MzZtUKy3hwwZ0uTn0cdo25rQmhGcaikAAJzgeItXrVIaO3asGbtm0KBBMnPmTCkpKTHdu9WYMWMkOzvblMwovdRtu3fvbgLNwoULzTg3zz33nIQEqqUAAIjscDN69GjJz8+XqVOnmkbEWs20aNEiTyPjnJwcUw1l0+Bzww03yC+//CJJSUly9NFHy2uvvWaeJyRQLQUAQGSPcxNsAR/n5qsXRRZMFDn69yIXv+7/5wcAIAIVNZdxbsIS80oBAOAowo2/US0FAICjCDcBK7mhQTEAAE4g3AQs3NAVHAAAJxBu/I1qKQAAHEW48TfGuQEAwFGEm0CNUEzJDQAAjiDc+FulXXJDuAEAwAmEG39jnBsAABxFuPE3qqUAAHAU4cbfqJYCAMBRhBt/o1oKAABHEW78jXFuAABwFOHGn3SCdaZfAADAUYQbf6ou14Tjvs70CwAAOIJwE4gqKRVPyQ0AAE4g3PiTXSUVkyASHeP03gAAEJEIN/7EjOAAADiOcBOIMW6okgIAwDGEm0CMTswYNwAAOIZw409USwEA4DjCjT9RLQUAgOMIN/7E1AsAADiOcBOIcMPUCwAAOIZwE4hB/Ci5AQDAMYQbf6JaCgAAxxFuAlItRYNiAACcQrgJSLUUXcEBAHAK4cafqJYCAMBxhBt/YpwbAAAcR7jxJ6ZfAADAcYQbf2L6BQAAHEe48SeqpQAAcFxIhJtZs2ZJly5dJDExUQYPHiwrVqxodNsXXnhBhg0bJq1atTLL8OHD97t9UFEtBQCA4xwPN/PmzZOJEyfKtGnTZNWqVdK3b18ZMWKE5OXl+dx+6dKlcskll8iSJUtk2bJl0rFjRznjjDNk69at4riq2pIbwg0AAI6JsizLcu7lxZTUHH/88fLMM8+Y2zU1NSaw3HTTTTJp0qQDPt7lcpkSHH38mDFjDrh9UVGRpKenS2FhoaSlpYlfPdpdpLRA5PovRDKP9e9zAwAQwYoO4vztaMlNZWWlrFy50lQteXYoOtrc1lKZpigtLZWqqipp3bq1z/srKirMAfFeAoZqKQAAHOdouCkoKDAlL5mZmXXW6+3t27c36Tnuuusu6dChQ52A5G369Okm6dmLlgoFhBaAMYgfAACOc7zNzeF4+OGHZe7cufLOO++Yxsi+TJ482RRh2Utubm4AS21qa/jiCTcAADgl1rFXFpGMjAyJiYmRHTt21Fmvt7Oysvb72Mcff9yEm48//liOO+64RrdLSEgwS8DZpTaKkhsAACKz5CY+Pl4GDBggixcv9qzTBsV6e8iQIY0+7tFHH5UHH3xQFi1aJAMHDpSQYIeb2ESR6Bin9wYAgIjlaMmN0m7gY8eONSFl0KBBMnPmTCkpKZFx48aZ+7UHVHZ2tmk7ox555BGZOnWq/O1vfzNj49htc1JTU83iGGYEBwAgJDgebkaPHi35+fkmsGhQ6devnymRsRsZ5+TkmB5Utueee870srrgggvqPI+Ok3PfffeJ82PcMDoxAAARPc5NsAVsnJuc5SKvnSeSfoTIjcv997wAAEAO5vzteMlN2Og0WOTure4u4QAAwDHNuit4SIqKcnoPAACIaIQbAAAQVgg3AAAgrBBuAABAWCHcAACAsEK4AQAAYYVwAwAAwgrhBgAAhBXCDQAACCuEGwAAEFYINwAAIKwQbgAAQFgh3AAAgLBCuAEAAGElViKMZVnmsqioyOldAQAATWSft+3z+P5EXLjZu3evuezYsaPTuwIAAA7hPJ6enr7fbaKspkSgMFJTUyO//vqrtGjRQqKiovyeKjU05ebmSlpamkQqjoMbx2EfjoUbx8GN47APx6Lpx0HjigabDh06SHT0/lvVRFzJjR6QI444IqCvoX+YSP6Q2jgObhyHfTgWbhwHN47DPhyLph2HA5XY2GhQDAAAwgrhBgAAhBXCjR8lJCTItGnTzGUk4zi4cRz24Vi4cRzcOA77cCwCcxwirkExAAAIb5TcAACAsEK4AQAAYYVwAwAAwgrhBgAAhBXCjZ/MmjVLunTpIomJiTJ48GBZsWKFRJr77rvPjPrsvRx99NES7v7973/LyJEjzaiZ+p7ffffdOvdrm/2pU6dK+/btJSkpSYYPHy4//fSTROKxuPLKKxt8Rs4880wJJ9OnT5fjjz/ejILerl07GTVqlKxfv77ONuXl5XLjjTdKmzZtJDU1Vc4//3zZsWOHhJumHItTTjmlwWfiuuuuk3Dy3HPPyXHHHecZoG7IkCHyr3/9K+I+D88d4Dj487NAuPGDefPmycSJE003tlWrVknfvn1lxIgRkpeXJ5Hm2GOPlW3btnmWzz77TMJdSUmJ+ZtrwPXl0Ucflaeeekpmz54ty5cvl5SUFPP50C+0SDsWSsOM92fkjTfekHDy6aefmhPVl19+KR999JFUVVXJGWecYY6N7bbbbpP3339f3nrrLbO9Tglz3nnnSbhpyrFQ48ePr/OZ0P8z4URHxX/44Ydl5cqV8vXXX8upp54q55xzjqxduzaiPg9HHOA4+PWzoF3BcXgGDRpk3XjjjZ7bLpfL6tChgzV9+nQrkkybNs3q27evFcn0v9Q777zjuV1TU2NlZWVZjz32mGfdnj17rISEBOuNN96wIulYqLFjx1rnnHOOFUny8vLMsfj00089f/+4uDjrrbfe8mzzww8/mG2WLVtmRdKxUCeffLJ1yy23WJGmVatW1v/93/9F9OfB+zj4+7NAyc1hqqysNClUqxq856/S28uWLZNIo9UtWiXRrVs3ueyyyyQnJ0ci2ebNm2X79u11Ph86N4pWXUbi50MtXbrUVFH07NlTrr/+etm5c6eEs8LCQnPZunVrc6nfF1qC4f2Z0OrbTp06hf1nov6xsL3++uuSkZEhvXv3lsmTJ0tpaamEK5fLJXPnzjWlV1otE6mfB1e94+Dvz0LETZzpbwUFBeaPlJmZWWe93l63bp1EEj1hv/zyy+akpcWJ999/vwwbNkzWrFlj6twjkQYb5evzYd8XSbRKSovbu3btKhs3bpS7775bfve735kv8ZiYGAk3NTU1cuutt8oJJ5xgvqyV/t3j4+OlZcuWEfWZ8HUs1KWXXiqdO3c2P4q+/fZbueuuu0y7nPnz50s4+e6778xJXKujtV3NO++8I7169ZLVq1dH1Ofhu0aOg78/C4Qb+I2epGzaaEzDjn5Q33zzTbn66qsd3TeEhosvvthzvU+fPuZz0r17d1Oac9ppp0m40fYmGu4joe3ZoR6La6+9ts5nQhve62dBw69+NsKF/ujTIKOlV2+//baMHTvWtK+JND0bOQ4acPz5WaBa6jBp8Zn+4qzfsl1vZ2VlSSTTXyJHHXWUbNiwQSKV/Rng8+GbVl/q/6Fw/IxMmDBB/vnPf8qSJUtMQ0qb/t21OnvPnj0R85lo7Fj4oj+KVLh9JrR0pkePHjJgwADTi0wb3j/55JMR93mIb+Q4+PuzQLjxwx9K/0iLFy+uU/yqt73rESNRcXGxSdyaviOVVr/oF5T356OoqMj0mor0z4f65ZdfTJubcPqMaFtqPZlrcfsnn3xiPgPe9PsiLi6uzmdCi961fVq4fSYOdCx80V/1Kpw+E77oeaKioiKiPg/7Ow5+/yz4pVlyhJs7d67p/fLyyy9b33//vXXttddaLVu2tLZv325Fkttvv91aunSptXnzZuvzzz+3hg8fbmVkZJgeEuFs79691n//+1+z6H+pGTNmmOtbtmwx9z/88MPm8/CPf/zD+vbbb01voa5du1plZWVWJB0Lve+OO+4wPUD0M/Lxxx9b/fv3t4488kirvLzcChfXX3+9lZ6ebv4vbNu2zbOUlpZ6trnuuuusTp06WZ988on19ddfW0OGDDFLuDnQsdiwYYP1wAMPmGOgnwn9P9KtWzfrpJNOssLJpEmTTA8xfY/6HaC3o6KirA8//DCiPg+T9nMc/P1ZINz4ydNPP20+nPHx8aZr+JdffmlFmtGjR1vt27c3xyA7O9vc1g9suFuyZIk5kddftNuz3R18ypQpVmZmpgnBp512mrV+/Xor0o6FntDOOOMMq23btqbra+fOna3x48eH3Y8AX+9fl5deesmzjQbbG264wXSDTU5Ots4991xz0g83BzoWOTk55uTVunVr83+jR48e1p133mkVFhZa4eSqq64yn3f9btTPv34H2MEmkj4PV+3nOPj7sxCl/xx8eQ8AAEBoos0NAAAIK4QbAAAQVgg3AAAgrBBuAABAWCHcAACAsEK4AQAAYYVwAwAAwgrhBkDE04k7o6KiGszvA6B5ItwAAICwQrgBAABhhXADICRmBp4+fbqZNTopKUn69u0rb7/9dp0qowULFshxxx0niYmJ8tvf/lbWrFlT5zn+/ve/y7HHHisJCQnSpUsXeeKJJ+rcrzMP33XXXdKxY0ezTY8ePeTFF1+ss83KlStl4MCBkpycLEOHDjWzMwNofgg3ABynweaVV16R2bNny9q1a+W2226Tyy+/XD799FPPNnfeeacJLF999ZW0bdtWRo4cKVVVVZ5QctFFF8nFF18s3333ndx3330yZcoUefnllz2PHzNmjLzxxhvy1FNPyQ8//CDPP/+8pKam1tmPe+65x7zG119/LbGxsXLVVVcF8SgA8BcmzgTgKC1Rad26tXz88ccyZMgQz/prrrlGSktL5dprr5X/+Z//kblz58ro0aPNfbt27ZIjjjjChBcNNZdddpnk5+fLhx9+6Hn8H/7wB1Pao2Hpxx9/lJ49e8pHH30kw4cPb7APWjqkr6H7cNppp5l1CxculLPPPlvKyspMaRGA5oOSGwCO2rBhgwkxp59+uilJsRctydm4caNnO+/go2FIw4qWwCi9POGEE+o8r97+6aefxOVyyerVqyUmJkZOPvnk/e6LVnvZ2rdvby7z8vL89l4BBEdskF4HAHwqLi42l1rKkp2dXec+bRvjHXAOlbbjaYq4uDjPdW3nY7cHAtC8UHIDwFG9evUyISYnJ8c08vVetPGv7csvv/Rc3717t6lqOuaYY8xtvfz888/rPK/ePuqoo0yJTZ8+fUxI8W7DAyB8UXIDwFEtWrSQO+64wzQi1gBy4oknSmFhoQknaWlp0rlzZ7PdAw88IG3atJHMzEzT8DcjI0NGjRpl7rv99tvl+OOPlwcffNC0y1m2bJk888wz8uyzz5r7tffU2LFjTQNhbVCsvbG2bNliqpy0zQ6A8EK4AeA4DSXaA0p7TW3atElatmwp/fv3l7vvvttTLfTwww/LLbfcYtrR9OvXT95//32Jj4839+m2b775pkydOtU8l7aX0TB05ZVXel7jueeeM893ww03yM6dO6VTp07mNoDwQ28pACHN7smkVVEaegDgQGhzAwAAwgrhBgAAhBWqpQAAQFih5AYAAIQVwg0AAAgrhBsAABBWCDcAACCsEG4AAEBYIdwAAICwQrgBAABhhXADAADCCuEGAABIOPn/CNa7Bvqhd7sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M2q9npKa4z9OGgmKtL/DWGwpKtN7W/u353nW7n60LS8rde0hLSQr6u/D/rVT9O7HPnX3G7Hbjffb9fwNlgb8BWxfZ+7G/i6C/DwsrLrS48FIZZrbSTDTAjn1OvTS3ZFesvSVVjeqlb3GbHb8yn/fc9jr2b2BLWbm8yxai3Dbvu6bMfz147S5X3O7zrpdX7LP3XN6Oe58P+/uq/Ly4bSnJgc9O5TrZ7VthafDfXrW/ucDfYWmV27u1ydaj5/VWtBButsG+XO0EEA32B7s96d8CzY8//qhu3brp9ttvd9vmzZvn1jfeeKPuu+8+dezYUY0bN9by5ct13HHH6c4773Tj/zz11FMaNGiQFi5cqHbt2tX6GjbK8z333KN7771XDz74oM455xwtXbpUTZo0UbjZH6j9cdkXW6H9IZXYZe96UUlZlS8m96Ub+AKr/IK2L+Dg6/bHbrxY4X3Be9cr1tWOu121Lzl/aLHFTvr+tZ3wtudLLxTcF7t9aadXhgT7Irc9Lgr6gvafGN1ScTIKlfziMndCCif7grU99n8pxyr/MQ738UAlL/h5f4exwv4u7TvAleaU+/RHfrFbElWzBulRfX3CzTbYibTrmHej8to/3D7Q/dLblpycHKWnp7uxZWwUZ2PjABkLO0cddVTgvhZGrBu93x133KFXX31Vb7zxhq644oqtlg6dddZZ7vJdd92lBx54QLNmzdIxxxxT4/39vxR+W1+ggrIiFwrsl78/HPiDQXBY2FhUqsLisoog4/0aKKw4cdQVdkL2fpl6v1DrW/G0C8mST94vLwvMbl0Rnu228mprf2mFHQtbW2jZ4ou9qPRP76v9ys3OTHW/IN2SaevUirVdTw38wvTfVj891QUn9+utImzaPrpfbRY6/b/s3PZSt7brLoyWlqnIQmrFv7E/sNr786/9/CUjfukp3q9xKwHxr7PSUiu3VZQSWUmK/xdwlV+1wb9oK9b2y7is3MKu3HPYv5eVQNTPCLqcnlplu7f2ttvzuVKkwC/WUhUU+X/NeiU99v6tpMf/y9eqNKy0yt5Pmi2p3tq22QnSbUtJdu/Dfx/7FW377H6F+6tDXGmCt//+E2qgqqRibQfCF/T589aV11Xtc+ir+FzYa9ove7cftVz2Fu+yHYdNFX/Hm4qC/qaDrtsPgOCSTfu3ts9X4/rpalQvTY2y0tU4y1s3ykpT44q1f7tdt8+gvZb32fH/0PE+O/7PU+WPn8pt7vOTGnRMU5OV4T/GFcfff1vlfbySi0ApRkrQ9ZSqpRv+ajI7jvZ6VtJUfbGSKH+paPBi34v2b2ulcoESRytt9F9P8kqC7AeXu0/Fbf5Sx9pLJVW5reJ+pvLzY98j3ueotuv+z5t9TwT+5tzfmlcyWWVbekUpZcV2+3eLJsJNnOvdu/cWAyfedtttrorJxhEqLS11k44GjycUzN9Vv+ve3dwXt/sCTUpTw+xs/bj0N3XfsLmizrm86pdrSZFW5xbqttdm6beNoSv5si+djCrF0FVPAv4vKvvSTU9N8daBL2L/7d4ff+DUWfEe/df9JTDeaaHyun1JWGCxL9iqxe9eCLC17U846trtC8YfAPyBx/9FbqVX/tLF6l/U3gmz8ks88AWekhz4sosl9j4t2AUHHfcFmpYSs21bmtSP7pd4XWXfLfHWLsXej79atlVOZrR3J6ERbrbBTqJWghKt1/6zqvd6uvbaa90giFZV1alTJzdVwmmnneZmTnd1z6Vl7leE1W8vWr3JnWTMqk2l7nqw3PwirdtUtM330Cq7ahAIDgRWIhBcH1+9nYlXb155PZoNCaPJgoj/SzPe32dmsvdvjfgWb8EGsYVwsx1/gNtTNRRtVi1lvaG25dNPP9V5Q4dpwLEnuF/+f2zI1ZKff1H33v20YGWeu4+VvvgbiPnZ95C/eNyKY611SoOMFLVomFmtAZpXZFtSVKS0gnp668qDlZnJLxgAQOTE/lkb28V6OH3xxRf65Zdf1KBBA5WXV22nYvX/K3IL1bJtB73w0svqesDhLrBMvPcud19fReNaq7awQGclKe2bZLneLKZ90/raq3V24Pks3zSun1Fr0auv1J4nzG8aAIAaxGYlNnaYVTfZ7Ntdu3Z149QEt6GxRmuL1+S7Bn3X3HqnsnMaadjggbr6grPdvFw9993XNf7au022urRqWNEoNk05WelUDwAA6pwk39Ymd4pDeXl5rndRbm6usrMrSyJMYWGhm5ncJuiMh6oU+6ddu6lYK3I3u+vWy6N1TqYbXyLcjUnj7VgCAGL3/F0d1VJxyhoF/75hc2CcBevRsUujet5gXQAAxDHCTRyycQqWritw1VCmdU49N6ASvRMAAImAcBNnbFjxX9YVuC7cVkrTtkmWG4QNAIBEQbiJIzYKqpXY2IB61m27Q9MsN2okAACJhDNfnLChvX9dv9m1tbGB8KzrtnXrBgAg0RBu4qBH1JqNRVqZV+iu29QAVhWVqCP5AgBAuKnDrJTmt/Wb3VQJplmDDNfVm4bDAIBERripo0rLyrX0jwLXzsaijHXzbtogI9q7BQBA1BFu6iCb3PKXtQVubVPZt2ua5UYUBgAATL9QJ1lVlAWb9JRkdWzRICTBxuammjBhQkj2DwCAaKLkpo4pKSt3VVGmQ7P6zP0EAEA1lNzUMRsKStwM3lnpqQQbAABqQLipY3I3ez2jGmVVVkU99thj2mWXXVReXl7lvieddJIuuOACLV682F1u2bKlGjRooP33318ffPBBxPcdAIBIINxsi02aXpwfnaXahO1FJWUqKC5TkpKqTKlw+umna926dfroo48C2/744w9NmzZN55xzjjZt2qTjjjtO06dP1zfffKNjjjlGgwYN0rJlyyJ6KAEAiATa3GxLSYF01y7Ree2bfpfS6weubthc4tYNMlPd9Ap+jRs31rHHHqvnnntORx55pNv28ssvq1mzZjr88MOVnJysHj16BO5/xx136NVXX9Ubb7yhK664IqJvCQCAcKPkpg6NRGztbUyjGibCtBKaV155RUVFRe76s88+qzPPPNMFGyu5ufbaa7XXXnupUaNGrmpq/vz5lNwAAOISJTfbkpbllaBE67UrbC4pc92/babv7BrCjVUzWQB6++23XZua//u//9M///lPd5sFm/fff1/33XefOnXqpHr16um0005TcbHXfgcAgHhCuNkWm8ogqGooWvylNg0zU2ucNyozM1OnnHKKK7FZtGiRunTpov3228/d9umnn+r888/XySef7K5bSc4vv/wS4XcAAEBkEG7qACuRya1ob9M4K73W+1nV1AknnKB58+bp3HPPDWzfY489NHXqVFe6Y/NO3XrrrVv0rAIAIF7Q5qYOsEH7bPA+K7GxxsS1OeKII9SkSRMtXLhQZ599dmD7+PHjXaPj/v37u4AzcODAQKkOAADxhpKbOsBfJWXdv63NTW2s8fDvv/9e49QKH374YZVtl19+eZXrVFMBAOIFJTcxrrzcp9zCil5SW6mSAgAAHkpuYtzGohKVlfvcuDb105luAYh7NnhnaZE3xlbJZm+xSVeyd4mJzg07/Z585VJ5mbf2lW0xSOl2SU6R0uop6koKpaI8qTBPKsqtWOdJSSlSpwFSWmb4XtvaS/74X6lok7THUVJWE8Ukn8/rkBMlhJsYFxjbJivNNQYGEoJ9MS79VPrjZ+9klpohpdbzThqpFdfddrueWbk9OblyVPGCdRXLH0GX19W83U5MDVtJjXeTmuy25XpnQoXtQ+5vUl7F4i7/6r2uP7QEB5jA5QIvzNSkXhOpUTupUVspp13lZVvntJXqNVJY2bHdsExa/YO0al7letOqivBSHhRegsJMbe9nZ+zaR+pzsdT1JCk1DKXZm9dLc1+Wfp9TNbgE1rlS2VaG0Wi+l3TKo1LryoFTQybvd+n1y6XFFc0MklOl3Q71jsWeJ0j1myrs//6FudKm1d6/uS35ayouB22zy/b+z3lJ0ZLks644CSQvL085OTnKzc1VdnZ2ldsKCwv1888/a7fddnNdq6OtrLxcP6zY6HpL7dGigeql150sGmvHEhXsZLP+F2ntT9LaH70vcjupW6mALQ13kRq08H4hR0NZqfTDa9KnE6SVc3f88Sl2skuSyrzBLEOmQcsagk8HqbSwhgBj61+lwg1//nWT07zxriwgFG/c9v0zsiuDjoWerGbeL/t6jSvXFpDscnqDrf+yts/Gqh8qAsz3FZfnb99+REL9FlKv86Xew73P7p9hoeyX/5O+eVqa/6b377o90htKmdnecc/MkdYtkgrWeqHj0Bulg0ZKKSH63v5+qvTWSO9zZYG+SUfv38bPSo06HOQFnb0GeX/HO3ssNvwirfhOWvmdtGahtHFlZXjZ3r+tlt2lSz9RpM7fMRduJk6cqHvvvVcrV650UwQ8+OCD6tOnT433LSkp0bhx4/Sf//xHv/32mxvL5e9//7ubKymU4cYa4NpAd9H2R36xfl1foIzUFHVu2aBOldxs3rzZNVIm3ESJFVmv+6kyxLjlJ+/Ld2u/Ov1fktUDT3bQkrOrd/IM5efRSjm+eUaa+ZBXMmDspN6un1Re4lUDlG6uqK6puOzWhd7tNUnJkOpXnNyzmtawBG23E739Kl7/s1da5F//seTPhRR73uw2Uk6bivWuFa9X3yt5svdYZV1tW0rQgJ32i3nDcu/45Fasgy9bCdSOBqdA6KkIPFbyYycyCzIbf6/9cc27SC26Si27Si329oKUndCTkr3FwrF9jgKXK7ZXv76jLHDZ5+SrydLGFd42ex07mVtpTvv+O/a5tH/zOc96z2mh38/eU9cTvX8rCy0uvGRXXWc03PJHQP5a6a2rvYBk2vSWTp4kNdtjx99r4D1vkN65Tpr7ond9l32lkx+TmneW1i6S5r8u/fC6tOLboAclSe0PrAw62a1rfu6yEmnNgsogYz8obLESqq2xY2Lh0kJ/g+B18OVWUsOWSshwM2XKFA0dOlSTJk1S3759NWHCBL300kuuK3OLFlumzhtuuEHPPPOM/vWvf2nPPffUu+++q1GjRumzzz7Tvvvu+6cPTllZmX788Uf32k2bhrl4bzssWbNJm4pK1TI70y11iU3kuXr1anXu3FkpKbQVClspjJUQ/LFYWrc4KMj85FV/1MZ+9TXdw/vCtZO/nczsS95OFra4aoRtsCoRq+/f42hpt4N3vi2InQxmPeYtduIydkLpe4m0/0Xb157ASnss5PgX2387Wds+hSKA2X4FBx63/sU7GVp1mIWW6gHGf91OgpH6UeKqwX4NCj1WBbZO2mzVb+u99+Eu/7H9v77t39kFGAsye3tL005VQ1c02El5wVvSrH9LS4NKB2w/+4yQ9hlS+2eytFj6cZpXSrPog8rPu5XCdD9V2m+otMt+O//vZqfU7170AolVa1l16VF/lfYf4VWb7oglH0uvXeqVBloYPPha6dDraz7+9tmc/4YXdH6bHXRDktS2rxd0Wu/jlb5ZELIwY5dr+rFjJaB2LO3+VgJjn2V/aLFQE842RfEQbizQ2FQBDz30kLtuA8u1bdtWf/nLX3TjjTducf9ddtlFN998c5VuzKeeeqorZbHQE4qDs2LFCm3YsMEFnKysrKiVlti4Nj+v2eRqqndrVl/pqXUjINjHqaCgwAUbm8eqdetafjHEu01rpOVfeL+K7BdeoDogqFpge0589udpxcFW4uJCzCIvyNhipQpbO0nVby416+yFmGZdKi9bqUttX7IWFPJXS3krvC9UCzuuyuX3ym120gwuLbESEisOt6Bjgafp7ts+PrbvMyd6v5j9VQBWzdP/L1LPc2Kj0Wi8ss+UteuxkBMceDZXXLfPp4WYFnt5v9BjnbX5mfUv6bspFe2VrHouR9r3HC8g+z+PVr3y9VPSty94VUd+7fpL+53nnfxD2WDb/k5ev0Ja8pF33drGDH7YC7/bYqWS02+XPp/oXbcqKCutabv/9r32hmVe6ZEFHfse2ho7Vq26e0Gm1T7e2r4roh1g62q4sXmNLDzY7NWDBw8ObB82bJgLF6+//voWj7HSlHvuuUcXXnhhYJuNxPvJJ59s9zgt2zo4djisisz2IZo2FZa6WcDTU5PVomGG6hoLNq1atapTVWk7zeqorQpo2efeF4mtLYhsixXju9ATFHhsbSeUTSsrgsySrbdxsF9Y1gbEfk3bl7hVF9gXk10PVy+K4gKvfcJP70k/viflVpuAtcnulUHHisaDf+X99rX02QPel67/F7MVsx94lbTXidFr64O6z6pv5jwnffkvLzz77X6kVLyp6kneSiF6nCXte57UrFP49slOr1/+W3rvVq8q1X7QHHuP1OPM2n/YWBXR1IulNfO9672GS0f/TcposHP7kPubV8o17zWvCtNCqz/E2Np+VNSR7+k6EW5ssLk2bdq4KqV+/foFtl9//fX6+OOP9cUXW6ZNG3X322+/1Wuvvabdd99d06dP10knneSqk/yzYVdn24Nvs4NjpUPbOjj2nNbGJ1oue2a2Fq7aqCsO76ST99uOpB9D0tLSYrMqykKIFcXaLynrlWKNLa13gZVwuMvNvHCxrQaA9tjfv6kMM7b4q1Sq95qwHgNWMhGoGqj4lWxfdNvLiqOttMUFmIoQ45ZOFaUwUTzW9vVhVWEWdGxZ+plUXlp5u7UbsV+sHQ70bv/5f5W3dTrKCzVW6lNHvlxRB9jfufUmsqpO+8z5e2pZ25zOA71AY8E7kiUTVtL66v+Tfv3Su249m06YIDVoHrTfZV7w//BOr2TUqn9OesjbZ+xwuKk73W8k3X///RoxYoRrb2MlAhZwhg8frsmTJ9f6GGuA/Ne//nWHX8tOztE6Qf+8Nl8fLtrgplsY0L2tMjOrldxYo0I7odov3XB0hYy36iH7ols83Vtbt8WtSvJKUlxD1IrA479sRd523K2LaPVGrFav3qaX1K6v1PYAr/jYnmdrAckCkb86IHhtDVjti80fYOyXlXV9jkUWSqy0yBarUrLusj9/XBF23veqtWxMDlv8pVXdTvPu26pbtPce8ciqXPcY4C3WDsXav1g15z5neA3lo8H+lodPkz67X/ponFeSYj+OBt0v7XWC137r1UukZTMrw4/dZt892Cl1qloquFeTNVi1NjjWNuett95yk0WGsuQmmu7/4Cf984MfdUjn5nrqgmo9x76dIr19jVdVYUX/ZzwdtcZdMckaGi6f5YUZayxYpQdBRe+V3Q6RGrb26t3zKxa7bMFie8fjsGJta6TXzoLMAV6dNUGzKvtqsS7ErkRnpheArKGw9awBEpVVO1kpjr8bd+djpF8+8arOrFHzsX+Xep5NaWZdLblJT09Xr169XNWSP9xYg2K7fsUVV2z1sda12Kq0rNrolVde0RlnnFHrfTMyMtxSV1jWfH3Ob+7yST2Cxm6wX8TvXOs1mvOzk8aUc6QhzyR2A8z1SyvCzHSvd0H1NioWPGzUUKt7t0BSWwixxrRWmmKlO9WDj63ty2bX/b3nqEP11FFjx8eOvS0HR3tngBhhbV0uniF9dJf06f1ezy1jwx5Yt3H7bsGfFtVqKevGbSU1vXv3dmPbWFfw/Px8V9VkrJu4hRirWjLWDsfGt+nZs6db33bbbS4QWTudePH9b3lasjZfGanJGtitogh1+ZfSKxdKG5Z69caH3eidZF842yudeP4s6cznpPQsxRWrg3YBwz/yZQ1rayAXPD6Fvyvx7kd4YcbW2zvWgrW1sTrw4HpwAAg1q2a27uFdjpU+vkfqeJjU73Ia1MdLuBkyZIjWrFmjMWPGuB5KFlqmTZumli29k9GyZcvcTNfB1VG33HKLlixZogYNGui4447T008/7XrmxAt/qc2Ari3VIC1J+t99XsK34cxtzIlT/+216zDnvCw9e7rXQPb5IdJZL9TduWesHYv1crDuxv7wYiUm2zPmigW+tn2kThZmjpRa99zx8SQAINKsWvu8qdHei7gU9RGKY7nOLtJsgsx+46Zr9cYi/efUNjp03i1el1vT7VTp+PFbzh1jjdKeOdWrr21/kHT2lJ3vMhgtP7whTR1Ry5DnSV5vpi1Gv2xZedl6JNWF8TgAAPHd5gZb+mLJOhdsTs6crUOmX+b1mkmrLx1/nzcmQ01tPFzyf1V6+hRvpM5nT/MmK7OB42Kd5Wobbt/GgLCGvNYuxkKcG9a7IsBYFVOo5mYBACQEzhox5J2vl+iu1H/rbH0oFVYMbnbq49se8dWqZIa+5gUc60po63Nf8eZAiVXWePe/13lzxBgbmvyYuwkyAIA/jYYJMaJo+Rxd8MP5Ojv1Q/msKubAq6UL3tu+oezNrr29gGPVM7/Okp4+2ZtoLxYVbZSeP7Mi2CRJA++SjruXYAMACAnCTUxUzTystCcGqKN+0xo1lu/c17yW9Ds6bkqb/aShb3iDx/32lfTU4JpHzo0mGwp88rHSove9ge+sG7v1EqBbNQAgRAg30fbWSOnd0UouL9H7Zb30zL7PK7nTYTv/fLv0lIa96U0j8PvX0lMnVQxOFyODV/37SGnVXK9dzfC3vdE5AQAIIcJNtNmsyJLuKjtPI0pG6ajeXf/8c9qgaee/5U0ZYCP0PnWilL9OUWUTLE4+xhuOv/me0kUfeNMVAAAQYoSbaCotDsxR9ELJIdq9eQPtvUuIGgG33Fs6/22vhGTlXOk/g7wB8aJh1r+8cXhK8r3Bqi54V2rcPjr7AgCIe4SbaLKxaSoUKEODe7ZxE4KGTIs9vYDToJW0ep705AnSxpWK6Oy8797sTRthg/Hte6438GD1sXoAAAghwk00Fee7VZEvTaVK1Yk9g+aSCpXmnb2AYxNFrpkvTewrzf6PFzzCqbhAevE8bxwbc+QY6cSHpJS08L4uACDhEW5iINxYqU3Pto3UvmmYpk5o1skLONYWxwYGfPNK6YljpdXzw/N6Nn3Cf06QFrwlpWR4Y/UcfA09ogAAEUG4iSZrgyIpX5kaHI5Sm2A2Xs6IGd6YMjbq8fLPpUkHSR/c5pWyhILNC/X+GOnB3tJvs70eW8PekLqfFprnBwBgOxBuoqis0GtzU+DLcBNlhp0NkmdjylwxS9rzBKm8VPrkn9LDB0g/fbDzz7vqB+m1y6QJ3aVP75eKcqUWe3s9omx6CAAAIoghYaMoL3eDGrtqqUztnlMvci+cs6t05rPSgreld66TNiyVnj1V2vtkbwqEhq22b/BBm9Tz0we8Afn82vWXDrxS2mMgM3MDAKKCcBNFuXleuClNyVJKchTao+x5vLTbIdJH46QvHpHmvSotmu41/u19gZScUvOcUD+8Jn32oLRijrctKVnaa5DU/0pvGggAAKKIcBNFm/I2uHW5tYGJFps9/Ji7pB5DpDev9kY1tq7b3z4vnTBBar2Pd7+iTdI3T7upIpS7zNtm0ydY9+5+l0lNOkbvPQAAEIRwE0UFm7yJLZPSs6K9K1LrHl4bmS8fl6bf7jUIfuww6YBLpdQMb7v1tDI28nGfi6X9L5LqN432ngMAUAXhJooKCza6dXJGA8UEq4bqe7FXxTTtRq/6yT9OjWmyu9T/CqnHWVJaBNsIAQCwAwg3UVRcEW7S6jVUTMluLZ3xH28+qPdv9bp0Wy+rLsfRSBgAEPMINzHQFTyjfojmkwq1zkd7CwAAdQg/w6PIVzFCcWashhsAAOogwk2U+Hw+JZV4JTcNGuZEe3cAAIgbhJsoydtcqszyQne5YTbhBgCAUCHcRMnKvEJlJXnhJj3WGhQDAFCHEW6iZEXuZmWpyLuSHsVB/AAAiDOEmyhZZSU38kpulB4j49wAABAHCDdRsjK3SPWTKLkBACDUCDfRbHMTKLkh3AAAECqEmyhZtSG/ss1NNCfOBAAgzhBuouSPvI1KTvJ5Vyi5AQAgZAg3UbIxz5sR3EmLgVnBAQCIE4SbKCgsKVPx5jx32WfBhskoAQAIGc6qUbA6r0j1A2Pc0A0cAIBQItxEuadUEu1tAAAIKcJNlEYnrl8x9QKNiQEACC3CTdRHJybcAAAQSoSbKI1OzLxSAACEB+EmClbmUS0FAEDchpuJEyeqQ4cOyszMVN++fTVr1qyt3n/ChAnq0qWL6tWrp7Zt22rkyJEqLKwICnXEylwmzQQAIC7DzZQpUzRq1CiNHTtWX3/9tXr06KGBAwdq9erVNd7/ueee04033ujuP3/+fD3++OPuOW666SbVJausKziTZgIAEH/hZvz48RoxYoSGDx+url27atKkScrKytLkyZNrvP9nn32mAw88UGeffbYr7Tn66KN11llnbbO0J5aUl/uqNihmdGIAAOIj3BQXF2v27NkaMGBA5c4kJ7vrM2fOrPEx/fv3d4/xh5klS5bonXfe0XHHHae6Ym1+kUrLfUFtbqiWAgAglFIVJWvXrlVZWZlatmxZZbtdX7BgQY2PsRIbe9xBBx0kn8+n0tJSXXLJJVutlioqKnKLX16eN+1BtKzK9falcWqpZPNmUi0FAEB8NSjeETNmzNBdd92lhx9+2LXRmTp1qt5++23dcccdtT5m3LhxysnJCSzWCDnaoxObxmnF3gbCDQAA8VFy06xZM6WkpGjVqlVVttv1Vq1a1fiYW2+9Veedd54uuugid7179+7Kz8/XxRdfrJtvvtlVa1U3evRo12g5uOQmmgFnZe5mt85J8YcbqqUAAIiLkpv09HT16tVL06dPD2wrLy931/v161fjYwoKCrYIMBaQjFVT1SQjI0PZ2dlVllgouWnAODcAAMRXyY2xEpVhw4apd+/e6tOnjxvDxkpirPeUGTp0qNq0aeOqlsygQYNcD6t9993XjYmzaNEiV5pj2/0hpy6MTmwYoRgAgDgMN0OGDNGaNWs0ZswYrVy5Uj179tS0adMCjYyXLVtWpaTmlltuUVJSklv/9ttvat68uQs2d955p+rS6MQm0+etCTcAAIRWkq+2+pw4ZW1urGFxbm5uVKqojvzHDC1ek6+fcq5QWtEf0mWfSy32ivh+AAAQr+fvOtVbKh7Y6MQmpbTA20DJDQAAIUW4iaCNhSXaVFSqFJUpuYxB/AAACAfCTQTZtAumRWZZ5UZKbgAACCnCTRR6SrVvUNHMKTlVSkmP7k4BABBnCDcRtKJiAL+2Dcq9DWn1paSk6O4UAABxhnAThWqpNlkV4YYqKQAAQo5wE4XRiVvVq2hzQ7gBACDkCDdRaHMTaFBMuAEAIOQIN1EYnbhZeom3gW7gAACEHOEmCiU3TdL84YaSGwAAQo1wEyHFpeVal++Fm0apxd7G9Kzo7hQAAHGIcBMhqzcWymbxSktJUpbPPzoxJTcAAIQa4SbC3cBbZmcqOTCvFG1uAAAINcJNhNvbtMrOlIrzvY2U3AAAEHKEmwiPTtwyx8LNJm8j4QYAgJAj3ES4Wqp1lZIbqqUAAAg1wk2ErMyrqJbKCQo3afSWAgAg1Ag3EbLSXy1FmxsAAMKKcBPheaVaB5fcUC0FAEDIEW4iwOfzaVVFtRQlNwAAhBfhJgLWF5S4EYoN4QYAgPAi3ESwG3jT+ulKT02mWgoAgDAi3ESwG7jrKWVzMATGuaG3FAAAoUa4ifToxKVFkq/Mu4FqKQAAQo5wE8lu4FZyU1Ixr5RJI9wAABBqhJtIdgN3jYkrqqRSM6WU1OjuGAAAcYhwE8HRib15pegpBQBAOBFuIlgtxYzgAACEH+EmAlbmBo9OXFEtRXsbAADCgnATZgXFpcorLHWXqZYCACD8CDcRKrXJSk9Rw4xUqbiitxThBgCAsCDcRKinlLW3SUpKChrAj9GJAQAIB8JNJEcnNlRLAQAQVoSbSI5ObAg3AACEFeEmkqMTm0C1FOEGAIBwINxEanRiqqUAAIgIwk2kRif2V0v555Yi3AAAEL/hZuLEierQoYMyMzPVt29fzZo1q9b7HnbYYa7XUfXl+OOPV8yPTmyolgIAIL7DzZQpUzRq1CiNHTtWX3/9tXr06KGBAwdq9erVNd5/6tSpWrFiRWD5/vvvlZKSotNPP12xprSsXGs2FtVSLUVXcAAA4jLcjB8/XiNGjNDw4cPVtWtXTZo0SVlZWZo8eXKN92/SpIlatWoVWN5//313/1gMN2s3FavcJ6UkJ6lpgwxvI21uAACI33BTXFys2bNna8CAAZU7lJzsrs+cOXO7nuPxxx/XmWeeqfr1Yy8srKiokmrRMMMFHIdwAwBAWKUqitauXauysjK1bNmyyna7vmDBgm0+3trmWLWUBZzaFBUVucUvLy9PURvAzzBxJgAA8V0t9WdYqOnevbv69OlT633GjRunnJycwNK2bduIzysVaExsmFsKAID4DTfNmjVzjYFXrVpVZbtdt/Y0W5Ofn68XXnhBF1544VbvN3r0aOXm5gaW5cuXK1JWVJTcBLqBG6qlAACI33CTnp6uXr16afr06YFt5eXl7nq/fv22+tiXXnrJVTede+65W71fRkaGsrOzqyyRsiq3WrVUeblUQm8pAADits2NsW7gw4YNU+/evV310oQJE1ypjPWeMkOHDlWbNm1c9VL1KqnBgweradOmqjOjE/sH8DOU3AAAEJ/hZsiQIVqzZo3GjBmjlStXqmfPnpo2bVqgkfGyZctcD6pgCxcu1CeffKL33ntPsczf5iZQLeWvklKSlFYvejsGAEAc26lw89FHH+nwww8P2U5cccUVbqnJjBkzttjWpUsX+Xw+xTLbP3/JTY2jEydVdA0HAADRb3NzzDHHaPfdd9ff/va3iDbQrUvyNpeqsKS8apsbGhMDABCb4ea3335zJS0vv/yyOnbs6KZLePHFF92gfPD4S20aZaUpMy3F28ikmQAAxGa4sS7cI0eO1Jw5c/TFF1+oc+fOuuyyy7TLLrvoyiuv1LfffqtE5x+duOoYN0yaCQBAzHcF32+//dxYMlaSs2nTJjcnlHXvPvjggzVv3jwlqppHJ6YbOAAAMRtuSkpKXLXUcccdp/bt2+vdd9/VQw895AbgW7RokdsWi5NZRsrK3KIaSm5ocwMAQEz2lvrLX/6i559/3vUIOu+883TPPfeoW7dugdttEsv77rvPVVMlqpV5m2sfnTgtK0p7BQBA/NupcPPDDz/owQcf1CmnnOJGAK6tXY51GU9UgXmlapo0k2opAABiK9wET5dQ6xOnpurQQw9VolqZV1RDuKG3FAAAMdnmxqZCsIbD1dm2v//976HYrzpvZY29pWhzAwBATIabRx99VHvuuecW2/fee29NmjRJia6wpEzrC0q20hWcaikAAGIq3NgcUK1bt95ie/PmzbVixQolutUVVVIZqcluEL8ASm4AAIjNcNO2bVt9+umnW2y3bYncQ2qLAfxyMpUUPIdUINzQWwoAgJhqUDxixAhdffXVbqybI444ItDI+Prrr9c111yjROefeqFKN3BDtRQAALEZbq677jqtW7fOTbngn08qMzNTN9xwgxutONH5RyduHdxTyjC3FAAAsRlurKrFekXdeuutmj9/vurVq6c99tij1jFvEk2NoxMb2twAABCb4cavQYMG2n///UO3N/E8OrEh3AAAELvh5quvvtKLL76oZcuWBaqm/KZOnapEVuPoxIY2NwAAxGZvqRdeeEH9+/d3VVKvvvqqa1hsM4B/+OGHysnJUaJbVdPoxIa5pQAAiM1wc9ddd+mf//yn3nzzTaWnp+v+++/XggULdMYZZ6hdu3ZKZOXlvkCD4iptbspKpLKKEi6qpQAAiK1ws3jxYh1//PHusoWb/Px818h45MiReuyxx5TI1uYXqbTcJxvepnnDjC1LbQzVUgAAxFa4ady4sTZu3Ogut2nTRt9//727vGHDBhUUVHR3TlCrKnpKNW+QobSU5C3DTXKalJoepb0DACD+7VSD4kMOOUTvv/++unfvrtNPP11XXXWVa29j24488kglsuDRiaugpxQAALEbbh566CEVFnrtSm6++WalpaXps88+06mnnqpbbrlFiczf3obRiQEAqCPhprS0VG+99ZYGDhzoricnJ+vGG28Mx77V6akXthidmHmlAACIzTY3qampuuSSSwIlN6hqRcUYNwzgBwBAHWpQ3KdPH82ZMyf0exMHauwGbkr84YZqKQAAYq7NjU2YOWrUKC1fvly9evVS/fpVSyP22WcfJfroxLVXS1FyAwBAzIWbM888062vvPLKwDYb58bns/FdklRWVqZEDzctCTcAANSdcPPzzz+Hfk/iwMbCEuUXl9UyI7i/txThBgCAmAs37du3D/2exFF7m4YZqaqfkVpLyQ1tbgAAiLlw89RTT2319qFDhyoRrawYnXiLAfwMk2YCABC74cZGJA5ms4LbtAs2z1RWVlbChptaRyc2xRXTUlAtBQBA7HUFX79+fZVl06ZNWrhwoQ466CA9//zzSlS1jk5sGKEYAIDYDTc12WOPPXT33XdvUaqTSGodndjQWwoAgLoVbvyjF//+++9SoncDr7HkhnADAEDMtrl54403qly38W1WrFjhJtQ88MADleglN1t0AzdUSwEAELvhZvDgwVWu28B9zZs31xFHHKF//OMfSlTb1VuKiTMBAIi9aqny8vIqi41IvHLlSj333HNq3br1Dj3XxIkT1aFDB2VmZqpv376aNWvWVu+/YcMGXX755e51MjIy1LlzZ73zzjuKtuLScq3dtD3hhmopAABiruQmVKZMmeLmqJo0aZILNhMmTNDAgQNdz6sWLVpscf/i4mIdddRR7raXX35Zbdq00dKlS9WoUSNF2+qNXpVUWkqSmmSlb3mHEn9XcKqlAACIuZKbU089VX//+9+32H7PPffo9NNP3+7nGT9+vEaMGKHhw4era9euLuTYODmTJ0+u8f62/Y8//tBrr73m2vZYic+hhx6qHj16KFa6gbdomKnk5KSqN/p8TL8AAEAsh5v//e9/Ou6447bYfuyxx7rbtoeVwsyePVsDBgyo3JnkZHd95syZtTZk7tevn6uWatmypbp166a77rorJibqbFo/Q5cdtruG7N92yxtLCyVfuXeZcAMAQOxVS9mgfTYacXVpaWnKy8vbrudYu3atCyUWUoLZ9QULFtT4mCVLlujDDz/UOeec49rZLFq0SJdddpkbIXns2LE1PqaoqMgtftu7fzuqQ7P6uv6YPWu+0d/exjD9AgAAsVdy0717d9deproXXnjBVS+FizVetvY2jz32mHr16qUhQ4bo5ptvdtVZtRk3bpxycnICS9u2NZSshJu/Siq1npScEvnXBwAggexUyc2tt96qU045RYsXL3bdv8306dPd1AsvvfTSdj1Hs2bNlJKSolWrVlXZbtdbtWpV42Osh5SVDtnj/Pbaay/XU8uquWoqTRo9erRrtBxcchPxgENPKQAAYrvkZtCgQa5Rr79a6JprrtGvv/6qDz74YIsxcGpjQcRKXywUBZfM2HVrV1MTa0Rsr2n38/vxxx9d6Kkp2BjrLp6dnV1liTgmzQQAIPa7gh9//PFu+TOsRGXYsGHq3bu3+vTp47qC5+fnu95TxmYXt+7eVrVkLr30UjcKss1f9Ze//EU//fSTa1B85ZVXKqYxOjEAALEdbr788ktXemJj0wT74osvXJWRhZXtYW1m1qxZozFjxriqpZ49e2ratGmBRsbLli1zPaj8rDrp3Xff1ciRI7XPPvu44GNB54YbblBMo1oKAICISfLZxFA7yEpZrr/+ep122mlVtk+dOtWNf2MhJ1ZZmxtrWJybmxu5Kqpvp0ivXix1PFwa+lpkXhMAgDiyI+fvnWpz88MPP2i//fbbYvu+++7rbkM1DOAHAEDE7FS4sUa61Xs5GZsZPDU1qjM6xCaqpQAAiO1wc/TRR7su1lY0FDyh5U033eTmfkJt80oRbgAACLedKma57777dMghh6h9+/auKsrMmTPHNQR++umnQ72PdR/VUgAAxHa4sV5K3333nZ599ll9++23qlevnuu+fdZZZ7lB9lBbtRRdwQEACLedbiBTv359HXTQQWrXrp0bHdj897//desTTzwxdHsYD2hzAwBAbIcbm8Dy5JNP1ty5c5WUlCTrTW5rv1iYpTsmww2TZgIAEJsNim3gvN12202rV69WVlaWvv/+e3388cdu8L4ZM2aEfi/rOkYoBgAgtktuZs6cqQ8//NBNfmkjCNuoxFZFZdMk2FQI33zzTej3tC5jbikAAGK75MaqnRo2bOguW8D5/fff3WXrPbVw4cLQ7mE8oM0NAACxXXLTrVs310vKqqZsfql77rnHzcr92GOPqWPHjqHfy7qOaikAAGI73Nxyyy1u9m5z++2364QTTtDBBx+spk2basqUKaHex7qPkhsAAGI73AwcODBwuVOnTlqwYIH++OMPNW7cuEqvKVQPN/SWAgAg3EI2EVSTJk1C9VTxpbxMKt3sXaZaCgCA2GxQjJ2YV8pQLQUAQNgRbiJVJZWULKVmRntvAACIe4SbSM4rRXskAADCjnATbswIDgBARBFuwo15pQAAiCjCTbgxxg0AABFFuIlkmxsAABB2hJtwo+QGAICIItyEG+EGAICIItyEG5NmAgAQUYSbcKPkBgCAiCLchBuTZgIAEFGEm3AroeQGAIBIItyEG13BAQCIKMJNuNHmBgCAiCLchBvhBgCAiCLchBtdwQEAiCjCTbgxcSYAABFFuAk3qqUAAIgowk240VsKAICIItyEGyU3AABEFOEmnEqLpfIS7zLhBgCAiCDcRKKnlCHcAACQOOFm4sSJ6tChgzIzM9W3b1/NmjWr1vs++eSTSkpKqrLY42K6SiolXUpJi/beAACQEKIebqZMmaJRo0Zp7Nix+vrrr9WjRw8NHDhQq1evrvUx2dnZWrFiRWBZunSpYhLtbQAASLxwM378eI0YMULDhw9X165dNWnSJGVlZWny5Mm1PsZKa1q1ahVYWrZsqdieNJOeUgAAJES4KS4u1uzZszVgwIDKHUpOdtdnzpxZ6+M2bdqk9u3bq23btjrppJM0b948xSRKbgAASKxws3btWpWVlW1R8mLXV65cWeNjunTp4kp1Xn/9dT3zzDMqLy9X//799euvv9Z4/6KiIuXl5VVZIoZwAwBA4lVL7ah+/fpp6NCh6tmzpw499FBNnTpVzZs316OPPlrj/ceNG6ecnJzAYqU9EUO4AQAgscJNs2bNlJKSolWrVlXZbtetLc32SEtL07777qtFixbVePvo0aOVm5sbWJYvX66IdwVPI9wAAJAQ4SY9PV29evXS9OnTA9usmsmuWwnN9rBqrblz56p169Y13p6RkeF6VwUvEUPJDQAAEZeqKLNu4MOGDVPv3r3Vp08fTZgwQfn5+a73lLEqqDZt2rjqJXP77bfrgAMOUKdOnbRhwwbde++9riv4RRddpJhTXOCtCTcAACROuBkyZIjWrFmjMWPGuEbE1pZm2rRpgUbGy5Ytcz2o/NavX++6jtt9Gzdu7Ep+PvvsM9eNPOb4q6XoCg4AQMQk+Xw+nxKI9ZayhsXW/ibsVVRvXyt9+S/pkOulI24O72sBABDH8nbg/F3nekvVKbS5AQAg4gg3EamWItwAABAphJtwouQGAICII9yEUwm9pQAAiDTCTThRLQUAQMQRbiJSLUVXcAAAIoVwE060uQEAIOIIN5EIN2lZ0d4TAAASBuEmXGxsRKqlAACIOMJNuJRstoTjXaZaCgCAiCHchIu/1MZQLQUAQMQQbsLdDTytvhQ08ScAAAgvzrrhQk8pAACignATLoQbAACignATLoxODABAVBBuwoV5pQAAiArCTbhQLQUAQFQQbsKFaikAAKKCcBMujE4MAEBUEG7ChWopAACignATLkyaCQBAVBBuwt7mhmopAAAiiXATLsV0BQcAIBoIN+FCmxsAAKKCcBMuVEsBABAVhJtwoeQGAICoINyEPdzQWwoAgEgi3IQLg/gBABAVhJtwKaFaCgCAaCDchAttbgAAiArCTTiUlUqlhd5lqqUAAIgowk04q6QMJTcAAEQU4SacVVJJKVJKerT3BgCAhEK4CXdPqaSkaO8NAAAJhXATDjQmBgAgagg34UC4AQAgagg34UC4AQAgscPNxIkT1aFDB2VmZqpv376aNWvWdj3uhRdeUFJSkgYPHqyYwqSZAAAkbriZMmWKRo0apbFjx+rrr79Wjx49NHDgQK1evXqrj/vll1907bXX6uCDD1bMYV4pAAASN9yMHz9eI0aM0PDhw9W1a1dNmjRJWVlZmjx5cq2PKSsr0znnnKO//vWv6tixo2IO1VIAACRmuCkuLtbs2bM1YMCAyh1KTnbXZ86cWevjbr/9drVo0UIXXnjhNl+jqKhIeXl5VZawY14pAAASM9ysXbvWlcK0bNmyyna7vnLlyhof88knn+jxxx/Xv/71r+16jXHjxiknJyewtG3bVmHHjOAAACRutdSO2Lhxo8477zwXbJo1a7Zdjxk9erRyc3MDy/Lly8O+n1RLAQAQPalRfG0XUFJSUrRq1aoq2+16q1attrj/4sWLXUPiQYMGBbaVl5e7dWpqqhYuXKjdd9+9ymMyMjLcEp3eUoQbAAASquQmPT1dvXr10vTp06uEFbver1+/Le6/5557au7cuZozZ05gOfHEE3X44Ye7yxGpctqRkps0wg0AAAlVcmOsG/iwYcPUu3dv9enTRxMmTFB+fr7rPWWGDh2qNm3auLYzNg5Ot27dqjy+UaNGbl19e1RRLQUAQOKGmyFDhmjNmjUaM2aMa0Tcs2dPTZs2LdDIeNmyZa4HVZ1SXOCtCTcAAERcks/n80X+ZaPHuoJbrylrXJydnR2eF3n0UGnFHOnsl6TOR4fnNQAASCB5O3D+rmNFInUE1VIAAEQN4SYcCDcAAEQN4SYcCDcAAEQN4SbUrAkT49wAABA1hJtQKyuWfGXeZcINAAARR7gJV5WUYRA/AAAijnATav4qqdRMKSXqwwgBAJBwCDehRmNiAACiinATaoQbAACiinATrmop2tsAABAVhJtQo+QGAICoItyEGpNmAgAQVYSbUAsM4Ncg2nsCAEBCItyEGtVSAABEFeEm1Ag3AABEFeEm1JhXCgCAqCLchBolNwAARBXhJtRK6C0FAEA0EW5CjWopAACiinATtmopuoIDABANhJtQo80NAABRRbgJV7hJy4r2ngAAkJAIN6HGCMUAAEQV4SbUmFsKAICoItyEGm1uAACIKsJNKJWXSyX0lgIAIJoIN+EYwM9QcgMAQFQQbsJRJaUkKa1elHcGAIDERLgJ1+jESUnR3hsAABIS4SaUmFcKAICoI9yEEj2lAACIOsJNKDFpJgAAUUe4CSUmzQQAIOoIN6HEvFIAAEQd4SaUaHMDAEDUEW5CiWopAACijnATSpTcAAAQdTERbiZOnKgOHTooMzNTffv21axZs2q979SpU9W7d281atRI9evXV8+ePfX0008rJhBuAACIuqiHmylTpmjUqFEaO3asvv76a/Xo0UMDBw7U6tWra7x/kyZNdPPNN2vmzJn67rvvNHz4cLe8++67ip2u4FRLAQCQsOFm/PjxGjFihAsoXbt21aRJk5SVlaXJkyfXeP/DDjtMJ598svbaay/tvvvuuuqqq7TPPvvok08+UeyU3NBbCgCAhAw3xcXFmj17tgYMGFC5Q8nJ7rqVzGyLz+fT9OnTtXDhQh1yyCE13qeoqEh5eXlVlrChWgoAgMQON2vXrlVZWZlatmxZZbtdX7lyZa2Py83NVYMGDZSenq7jjz9eDz74oI466qga7ztu3Djl5OQElrZt2ypsSugtBQCAEr1aamc0bNhQc+bM0Zdffqk777zTtdmZMWNGjfcdPXq0C0P+Zfny5eHbMUpuAACIutRovnizZs2UkpKiVatWVdlu11u1alXr46zqqlOnTu6y9ZaaP3++K6Gx9jjVZWRkuCUiCDcAACR2yY1VK/Xq1cu1m/ErLy931/v167fdz2OPsbY1UUe4AQAgsUtujFUpDRs2zI1d06dPH02YMEH5+fmu95QZOnSo2rRp40pmjK3tvtZTygLNO++848a5eeSRR2KnK3ga4QYAgIQNN0OGDNGaNWs0ZswY14jYqpmmTZsWaGS8bNkyVw3lZ8Hnsssu06+//qp69eppzz331DPPPOOeJ+oouQEAIOqSfNafOoFYV3DrNWWNi7Ozs0P3xGUl0h3NvMvX/yxlNQndcwMAkODyduD8XSd7S8Ukf6mNoSs4AABRQ7gJdbhJTpNS06O9NwAAJCzCTajQ3gYAgJhAuAkVJs0EACAmEG5C2aDYgk0G4QYAgITuCh432vWVbvrNZvOM9p4AAJDQKLkJtaSkaO8BAAAJjXADAADiCuEGAADEFcINAACIK4QbAAAQVwg3AAAgrhBuAABAXCHcAACAuEK4AQAAcYVwAwAA4grhBgAAxBXCDQAAiCuEGwAAEFcINwAAIK6kKsH4fD63zsvLi/auAACA7eQ/b/vP41uTcOFm48aNbt22bdto7woAANiJ83hOTs5W75Pk254IFEfKy8v1+++/q2HDhkpKSgp5qrTQtHz5cmVnZytRcRw8HIdKHAsPx8HDcajEsdj+42BxxYLNLrvsouTkrbeqSbiSGzsgu+66a1hfw/5hEvlD6sdx8HAcKnEsPBwHD8ehEsdi+47Dtkps/GhQDAAA4grhBgAAxBXCTQhlZGRo7Nixbp3IOA4ejkMljoWH4+DhOFTiWITnOCRcg2IAABDfKLkBAABxhXADAADiCuEGAADEFcINAACIK4SbEJk4caI6dOigzMxM9e3bV7NmzVKiue2229yoz8HLnnvuqXj3v//9T4MGDXKjZtp7fu2116rcbm32x4wZo9atW6tevXoaMGCAfvrpJyXisTj//PO3+Iwcc8wxiifjxo3T/vvv70ZBb9GihQYPHqyFCxdWuU9hYaEuv/xyNW3aVA0aNNCpp56qVatWKd5sz7E47LDDtvhMXHLJJYonjzzyiPbZZ5/AAHX9+vXTf//734T7PDyyjeMQys8C4SYEpkyZolGjRrlubF9//bV69OihgQMHavXq1Uo0e++9t1asWBFYPvnkE8W7/Px8929uAbcm99xzjx544AFNmjRJX3zxherXr+8+H/aFlmjHwliYCf6MPP/884onH3/8sTtRff7553r//fdVUlKio48+2h0bv5EjR+rNN9/USy+95O5vU8KccsopijfbcyzMiBEjqnwm7G8mntio+Hfffbdmz56tr776SkcccYROOukkzZs3L6E+D7tu4ziE9LNgXcHx5/Tp08d3+eWXB66XlZX5dtllF9+4ceN8iWTs2LG+Hj16+BKZ/Um9+uqrgevl5eW+Vq1a+e69997Atg0bNvgyMjJ8zz//vC+RjoUZNmyY76STTvIlktWrV7tj8fHHHwf+/dPS0nwvvfRS4D7z589395k5c6YvkY6FOfTQQ31XXXWVL9E0btzY9+9//zuhPw/BxyHUnwVKbv6k4uJil0KtqiF4/iq7PnPmTCUaq26xKomOHTvqnHPO0bJly5TIfv75Z61cubLK58PmRrGqy0T8fJgZM2a4KoouXbro0ksv1bp16xTPcnNz3bpJkyZubd8XVoIR/Jmw6tt27drF/Wei+rHwe/bZZ9WsWTN169ZNo0ePVkFBgeJVWVmZXnjhBVd6ZdUyifp5KKt2HEL9WUi4iTNDbe3ate4fqWXLllW22/UFCxYokdgJ+8knn3QnLStO/Otf/6qDDz5Y33//vatzT0QWbExNnw//bYnEqqSsuH233XbT4sWLddNNN+nYY491X+IpKSmKN+Xl5br66qt14IEHui9rY//u6enpatSoUUJ9Jmo6Fubss89W+/bt3Y+i7777TjfccINrlzN16lTFk7lz57qTuFVHW7uaV199VV27dtWcOXMS6vMwt5bjEOrPAuEGIWMnKT9rNGZhxz6oL774oi688MKo7htiw5lnnhm43L17d/c52X333V1pzpFHHql4Y+1NLNwnQtuznT0WF198cZXPhDW8t8+ChV/7bMQL+9FnQcZKr15++WUNGzbMta9JNF1qOQ4WcEL5WaBa6k+y4jP7xVm9Zbtdb9WqlRKZ/RLp3LmzFi1apETl/wzw+aiZVV/a31A8fkauuOIKvfXWW/roo49cQ0o/+3e36uwNGzYkzGeitmNRE/tRZOLtM2GlM506dVKvXr1cLzJreH///fcn3OchvZbjEOrPAuEmBP9Q9o80ffr0KsWvdj24HjERbdq0ySVuS9+Jyqpf7Asq+PORl5fnek0l+ufD/Prrr67NTTx9RqwttZ3Mrbj9ww8/dJ+BYPZ9kZaWVuUzYUXv1j4t3j4T2zoWNbFf9SaePhM1sfNEUVFRQn0etnYcQv5ZCEmz5AT3wgsvuN4vTz75pO+HH37wXXzxxb5GjRr5Vq5c6Usk11xzjW/GjBm+n3/+2ffpp5/6BgwY4GvWrJnrIRHPNm7c6Pvmm2/cYn9S48ePd5eXLl3qbr/77rvd5+H111/3fffdd6630G677ebbvHmzL5GOhd127bXXuh4g9hn54IMPfPvtt59vjz328BUWFvrixaWXXurLyclxfwsrVqwILAUFBYH7XHLJJb527dr5PvzwQ99XX33l69evn1vizbaOxaJFi3y33367Owb2mbC/kY4dO/oOOeQQXzy58cYbXQ8xe4/2HWDXk5KSfO+9915CfR5u3MpxCPVngXATIg8++KD7cKanp7uu4Z9//rkv0QwZMsTXunVrdwzatGnjrtsHNt599NFH7kRefbFuz/7u4LfeequvZcuWLgQfeeSRvoULF/oS7VjYCe3oo4/2NW/e3HV9bd++vW/EiBFx9yOgpvdvyxNPPBG4jwXbyy67zHWDzcrK8p188snupB9vtnUsli1b5k5eTZo0cX8bnTp18l133XW+3NxcXzy54IIL3Ofdvhvt82/fAf5gk0ifhwu2chxC/VlIsv/teHkPAABAbKLNDQAAiCuEGwAAEFcINwAAIK4QbgAAQFwh3AAAgLhCuAEAAHGFcAMAAOIK4QZAwrOJO5OSkraY3wdA3US4AQAAcYVwAwAA4grhBkBMzAw8btw4N2t0vXr11KNHD7388stVqozefvtt7bPPPsrMzNQBBxyg77//vspzvPLKK9p7772VkZGhDh066B//+EeV223m4RtuuEFt27Z19+nUqZMef/zxKveZPXu2evfuraysLPXv39/Nzgyg7iHcAIg6CzZPPfWUJk2apHnz5mnkyJE699xz9fHHHwfuc91117nA8uWXX6p58+YaNGiQSkpKAqHkjDPO0Jlnnqm5c+fqtttu06233qonn3wy8PihQ4fq+eef1wMPPKD58+fr0UcfVYMGDarsx8033+xe46uvvlJqaqouuOCCCB4FAKHCxJkAospKVJo0aaIPPvhA/fr1C2y/6KKLVFBQoIsvvliHH364XnjhBQ0ZMsTd9scff2jXXXd14cVCzTnnnKM1a9bovffeCzz++uuvd6U9FpZ+/PFHdenSRe+//74GDBiwxT5Y6ZC9hu3DkUce6ba98847Ov7447V582ZXWgSg7qDkBkBULVq0yIWYo446ypWk+BcryVm8eHHgfsHBx8KQhRUrgTG2PvDAA6s8r13/6aefVFZWpjlz5iglJUWHHnroVvfFqr38Wrdu7darV68O2XsFEBmpEXodAKjRpk2b3NpKWdq0aVPlNmsbExxwdpa149keaWlpgcvWzsffHghA3ULJDYCo6tq1qwsxy5Ytc418gxdr/Ov3+eefBy6vX7/eVTXttdde7rqtP/300yrPa9c7d+7sSmy6d+/uQkpwGx4A8YuSGwBR1bBhQ1177bWuEbEFkIMOOki5ubkunGRnZ6t9+/bufrfffruaNm2qli1buoa/zZo10+DBg91t11xzjfbff3/dcccdrl3OzJkz9dBDD+nhhx92t1vvqWHDhrkGwtag2HpjLV261FU5WZsdAPGFcAMg6iyUWA8o6zW1ZMkSNWrUSPvtt59uuummQLXQ3Xffrauuusq1o+nZs6fefPNNpaenu9vsvi+++KLGjBnjnsvay1gYOv/88wOv8cgjj7jnu+yyy7Ru3Tq1a9fOXQcQf+gtBSCm+XsyWVWUhR4A2Bba3AAAgLhCuAEAAHGFaikAABBXKLkBAABxhXADAADiCuEGAADEFcINAACIK4QbAAAQVwg3AAAgrhBuAABAXCHcAACAuEK4AQAAiif/H7+TYW3MOlwUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iCEkRgWgSefa+SUSgel1DVFUZ1F/oh04VqpOp3B/KKsDBzEL1szuK/IxyIpbfhexX2bX5JRXqZi/rD2XXeC7Hj5x0pYWidbx2ApaAlBgeWOdYMg84x84Uq9YX7V57LC0f+aV1t10ChbR2RFUHIrmPCvY3PTYGJdkHEnD2Z+bjQKb2e5Fjsz6y31rFhSI1JhgVVQYUlVWgsLQSxWWVKCyrQJHcV3dBCVm3bJu2faU22ZdeXkBCWCCSo4KQFBmEZHWr+dzX21sdY/sy8rE3Ix/70vOxL6NABaiMvFJ1+3F/Vo31No0Kwi2XpuLOQa3gLAxAhJ49e9Z4XlBQgDlz5qjuLLlchczWXFxcjLS0tPOup0uXLqbHUiAdHh6OzMxMu203uQc5oWSroKN96Jvfy3JLSQuC1kKgBSJpaTAPOVrLgBZ2rCV/SUurQaIEj4hAJIQHIlpOXipUVWnBqkLutVtFddAyfk3ujV8vlpaRcu2kVVx9bzzhybarZRa0IEtXQ1KkbFfNYCTrOnSqAAezCtVJ9XBWgWqdOZeYEH80jQ5WLQb+vt7qZCv32mOtdcq03McbAX7avXTFBPppr5F7CWTyGrk3LpfXque+PqqVQUKGHAeyPfml5Sr8FFSHoPyScu2kbVpWjoLq57KfjNtg3A7j9hm3tea2easQICdjOb4kYPyZXYi8kgpsPnJG3czJcSSBqGVsiHrN+QJObbGh/vD28lJhWoXryiqcyi9VN0tJ6FXBTAJaQijaVrdcNY0KVsfihVRWhyMJRMZQJPfqOJRjUh2LxmO35uOy6uPXeMxK65yEGgk3ElLk2Jf9eiGdkiPUzZz8LuWPG2MgMgYk2UcSKGW7nIkByAbk4JXWGGe8ry3UHs310EMPYeXKlapbrHXr1ggKCsINN9yAsrLzn6xqX9JCPvTkul/kHPKBJi0s2QVlyC4sVY/lA07aNuRkJH+Jy0mzymAwLTM9r743/13Kx7B8Fstj9ZlcfS9f0ZaffZ2cwOTkYww7Z6rrBOqTEh2kuirUX7mVBu2EKCdC40mx+oQpN2MLgpys5GYJOREbT5wSnOSvfxVuJESEByIxIsgUeGJDAxp04rGWdHuUVFSpMCThqMgsHBWXV6gThHRBSN2FtExJS87JnBL1Gvk9ym3H8bwG/cypMSHqBN+q+kTfMi4UreJCVGuEI8mxoVq4/H0QH+a495X9K919ciI+UN3aKCfiP7OL1HG69WiOutUmx4AEAO0WXOOxBAT5OcxLIOQYP1NYpgLRmSK5L9OWqcfavfH/Qeu4UK2LrrqbTtbn3YjjTY5V7Q8C16q3CQv0Q/dmUepmTvaT/A4kxDsTA5CN/mM3tCvKmaQLrCGXnvj5559Vd9a1115rahH6888/HbCF1BDyob3zeK4qTpRgo+4LSlXQkRNjVqH2OLf43KHD0SQcNYsONtVkyF+68rhVfEiD/+/UbkEwthjIecPUGmDeOmBqLdAeN+YEY2tSgxEqNwvqIOTnl9+pBKOTuVKAWoKT1cWp8lhIsGkZG4qWcSGq60RO2PJeeiYtURc1CVc3c/LHgLQOSUiX+4ggv3oDTkM//+Um30cXFhXij94tnX8tTdc/a5PNyMitjRs3qjATGhp6ztaZNm3aYPny5arwWf5zz5w5ky05Tg48v/15GhsOyS0b24/nNrhGRc750SEBqrle6hGkq0Ca7bUGHOmakBYceZ322Lt6wdnXaOuRxiB5R9UqVKuVSGs9qm5Bqn6NdH1IuJHCR2nal5NxQ2tXXK0FwVXIzy+tNnLrkFTzZE6Wk3BsLM4lfWIA0hHp2powYQI6dOiganpkGHx95s2bh9tuuw19+/ZFbGwsHn30UeTlXbi5nRwXeLQ++kDEhASogtmYUC3kmJ6HaMsig/xcquWDiMhVeBnkzzaqQU72MmoqNzdXFfKaKykpweHDh9GiRQsEBgY6bRvdnafsx6rqgsstaTnYcjRHFSKaF+gab8YRTNow37Ojm2TYaUMCj9TJXNoiBpe2jEHvltGqmZ6IiBp+/q6NLUBEFpAaDCmY3JJ2Br+n5WBr2hmLi3HNyagZ4/Bgcww8RET2xQBEdJ7WnQOnCvD7kTOqhef3tDPqee02UwkxXZpGqpEO0g1lLNA1H86rjWgqNw39NQ57Ng5TZuAhInIsBiCi6rBz5HQRdp7Ixc4TedhxPFe19NQ3WZsM1744JRLdU7XhnTL1u3RlWToM2hiO5Htl+DURETkOAxDpjoQPacmReVRU4Dmeh10n81QgqW+upa4pEbi4ei6Li5tFqvlBGkuGJhtH9BARkeMxAJFHk7k+dp/Mw44TuSrw7DqRi93p+fXOEizDYi9KDEPH5Ah0TApH16aRaJ8Ypvt5VIiIPBEDEHkUKSaW7iu5FtAvB7PV6CqZZbc2GY0lc6lI0OmUFIGOyeFqrhpLu7KIiMg9MQCR29fu7EnPV4Fn/cEsdSXi2nU7chHCztXXqTEGHpmVmPPjEBHpFwMQuRWZtkrm3Vl/UGvhkXlzal9nSubX6d0iBn1axaBvqxi0Swhj2CEiohoYgMgtyNWNX161Hyu2HK9zteVgfx/0ah6two6Eno5JEXa9kCUREbk/BiCy6FpiDzzwgLo50ve7MjDrvztMF3yUi1v2SI0yBR6Zg4e1O0REZAkGIHJZmXklmPPFTny9Pd00WeDMqzpgYNu4Rl9Yk4iI9I0BiFyysPmjTWl47ts9qqBZurPuGNACDwxpq64ETkRE1FjsN9CJt956C0lJSaiqqjn/zahRo9SV3w8ePKgeJyQkIDQ0FL169cL333/v8O3cl5GP0W+uxxOf7VDhp2vTCHw+tR9mDL+I4YeIiGyGLUC2IBeHKi9y/Pv6BQNeDSv2HT16NO69916sWbMGQ4YMUctOnz6Nb7/9Fl9//TUKCgowYsQIPPPMMwgICMCiRYswcuRI7N27F82aNbPzDyLXxKrE/DUHsGDdQXVx0BB/Hzw0rB3G92nOgmYiIrI5BiBbkPDzjyTHv+9jJwD/kAa9NCoqCsOHD8dHH31kCkDLli1DbGws/vKXv8Db2xtdu3Y1vf7pp5/GihUr8Pnnn2Pq1Kmwp18OZuHxFTtwOKtQPR96UQKeGtURSZFBdn1fIiLSL3aB6cjNN9+MTz/9FKWl2jDyDz/8EGPHjlXhR1qAHnroIVx00UWIjIxU3WC7d+9GWlqaXWt9nv92D256e6MKP/FhAVhwS3e8Pb4Hww8REdkVW4Bs1RUlrTHOeF8LSJeWTCT41VdfqRqfH3/8Ef/617/U1yT8rFy5Ei+88AJat26NoKAg3HDDDSgrK7PLpucVlyEjrwT/25mpevFu7t0Mj1zZHuGBfnZ5PyIiInMMQLYgZ/AGdkU5U2BgIK677jrV8nPgwAG0a9cO3bt3V1/7+eefMXHiRFx77bXqubQI/fnnnzbfBglgmfmlSM8tQaUBaB4TjFdu6YQeqdE2fy8iIqJzYQDSYTfY1VdfjZ07d+KWW24xLW/Tpg2WL1+uWom8vLwwc+bMOiPGbBF+0vNKTDM5yyUr3rilJ8JDLWvJIiIiaizWAOnM4MGDER0drUZ33XTTTabl8+bNU4XSffv2VSFo2LBhptYhW4Wfk7lnw09cWCAigvzg78tDkIiIHI8tQDojBc8nTpyo9zIXq1evrrHsnnvuqfHc2i4xCT/HzxTjdJFWT5QcGYQQXwNyrVobERFR4/HPb7KrKoMBR09r4Udm80mJCkZMaICzN4uIiHSOLUBk12HuaaeLkFdSruqKmkUFISLY39mbRURExABE9gs/f2YXoqC0QoWf1OhghAdxiDsREbkGp3eBzZ8/X9WfyBDt3r17Y9OmTed8bXl5OZ566im0atVKvV5mLpZLOZibM2eOOuGa39q3b++An4SMKquq1MSGEn68vbzQIobhh4iIXItTA9CSJUswbdo0zJ49G7///rsKNDL6KDMzs97XP/HEE3jzzTfx6quvYteuXbjzzjvVvDVbtmyp8bqOHTvi5MmTpttPP/1k822Xwl6qq6JSCz+FZdpV3FvEhiC0nskNuf+IiEi3AUiGXk+ePBmTJk1Chw4dsGDBAgQHB2PhwoX1vv7999/HY489pi7a2bJlS9x1113q8Ysvvljjdb6+vkhMTDTd5HpXtuLnp53Mi4qccPFTF1deWYVDWYUoKquEr7cXWsaGICSg/l5W4wzTPj68wjsREemoBkhOgJs3b8aMGTNqDNEeOnQo1q9fX+/3yDWspOvLnFyyoXYLz/79+5GUlKRe26dPH8ydO/e8VzSX9RqvjyXy8vLO+Vo5Ycu1soytVBLYpJtN78oqqtRQ97LKSvh4eyM5IgheVRUoKamo81qZYPHUqVNq30lYJSIicjSnnX2ysrJQWVmJhISEGsvl+Z49e+r9Hukek1ajgQMHqjqgVatWqdmLZT1GUkf03nvvqcs8SPfXk08+iQEDBmDHjh0ICwurd70SkOR1DSWtSuJcXXV6I91eWQVlqKgyqJaf2FB/nCg8f+OihF0JpQyPRETkDG715/fLL7+susykqFlOnBKCpPvMvMts+PDhpsddunRRgSg1NRVLly7F7bffXu96pRVKapHMW4BSUlLOuR3y3k2aNEF8fLwqzNazo6cLMf2TbcgqKFUTHD5/QxckRlz4Su7+/v4qBBEREekqAEldjnQnZWRk1Fguz40tLLXFxcXhs88+Q0lJCbKzs1U31/Tp01U90LlId1Xbtm3VxT/PJSAgQN0sJduv5xoWKXa+5b0tyMgrRZv4ULx2a2/Eh9fsoiQiInJFTvsTXFoAevToobqxzGtD5LnU7ZyP1PYkJyejoqICn376KUaNGnXO18pVzQ8ePKhabMh2jmQXYtxbG1T4aZcQho+nXMrwQ0REbsOpfRDS7fT222/jP//5D3bv3q1GdRUWFqpuLTF+/PgaRdIbN25UNT+HDh3Cjz/+iCuvvFKFpkceecT0moceegjr1q1T16365Zdf1DB5aaUZN26cU35GT5SWXaTCj1zZXVp+Ppzcm5e3ICIit+LUGqAxY8ao0UCzZs1Ceno6unXrpiY2NBZGp6Wl1agTka4vmQtIAlBoaKgaAi9D46Wby+jYsWMq7EgXmXSZ9e/fHxs2bFCPqfGOni7CuLc34ERuCVrFhajwE8vwQ0REbsbLwBnp6pAi6IiICOTm5iI8PNzZm+Myjp0pwti3NuDYmWI1xw+7vYiIyF3P3xyGQw1yIqdYtfxI+GkeE4yPJjP8EBGR+2IAogs6mauFn6Oni5EaE4zFUy5FYgTDDxERuS8GIDqvjLwS3PT2RhzJLkJKdBAWT74UTRowzw8REZErYwCic8rMK1GjvWS+n6ZRWvhJimT4ISIi98cARPXKzC9R3V5ycVOZ4VnCT9OoYGdvFhERkU0wAFEdclmLm9/eiIOnCpEUEajCT0o0ww8REXkOBiCqIbugFDe9vQH7MwuQGB6oCp6bxTD8EBGRZ2EAIpPThWW4+d8bsS+jAAnhASr8pMaEOHuziIiIbI4BiEye+Gw79qTnIy4sQM3z0yKW4YeIiDwTAxCZru/1zY509fjdib3QKi7U2ZtERERkNwxApLz7y2HIRVEGto1Dp+QIZ28OERGRXTEAEfJKyrH016Pq8e39Wzh7c4iIiOyOAYhU+Cksq0Sb+FAMbBPr7M0hIiKyOwYgnausMuC9X/5Uj2/r3wJeXl7O3iQiIiK7YwDSue92pqsrvEcF++Hai5OdvTlEREQOwQCkc+/8dFjd39w7FYF+Ps7eHCIiIodgANKxP47m4LcjZ+Dn44XxfVKdvTlEREQOwwCkY8bWn5FdkhAfHujszSEiInIYBiCdOplbjK+3nzQVPxMREekJA5BOLVp/BBVVBvRuEc2JD4mISHcYgHSoqKwCH21MU4858SEREekRA5AOffr7ceQWl6NZdDCGXJTg7M0hIiJyOAYgnamqMuDd6uLnSf2aw8ebEx8SEZH+MADpzNp9mTiUVYiwAF+M7pni7M0hIiJyCgYgnVn4k3bZi7GXpCA0wNfZm0NEROQUDEA6sic9Dz8dyIL0ek3o29zZm0NEROQ0DEA6srC69md4pyZoGhXs7M0hIiJyGgYgncgqKMVnW0+ox7f1Z+sPERHpGwOQTnyw4QjKKqrQNSUS3ZtFOXtziIiInIoBSAdKKypVADJOfOjlxaHvRESkbxwGpAOfbz2BrIIyNIkIxPBOic7eHHIlFaVAaQFQmguU5te65Wn3Pv5AaMLZW1gCEBAONDZIGwxASS5QlA0UngLKi4DknkBgONxaVRVQUQyUy61I28dyq5T7slr3srys5n1VOdD0EqDFAGf/JEQejQHIwxkMBtNV32Xkl58PG/08ipxsJaiU5ADFZ6pv1Y/rLMs5G2qMNzkJW8M3CAiNB8IStfvQRC0YqZCUCPiHaMGmKAsorL4ZH6vAU30vJ/sa6w0E2o0AutwItBoC+PrD4SSYyX7KOwHkHdfuc48DhZlaqCkrPBtu5FZWVP28UHss4ccWmg8AhswCUi6BXY6bfd8CRzdov7PIZkBEinYfFNX4cOvp5Bj47V0gR2tZt4hfEBDdCohtA8S0AUJiub+dhAHIw60/mI096fkI8vPBuF7NnL05rk9OfnLSO7VHa50wP7md8+Rn9lhaS4IitVug3EdV36ofm5ZV3/uHAd7e2jrrBJcGPJfHhqrG/9x+IUBAWP03aZnIzwAKqm8SDuQkLx/+1pwAapN9EBKj/Rw5acDO5dpN9k/H67QwlNLbdicJafE686dZwKkOOab7E0BZgW3eyydAC3US5NTj2vcB2jFjug/UQumer4A/fwTeuRxoeyUw+AkgsXPjt0eOsz8+BtbPB7L31/8a/9CagSjSeC/LmtnnhC2BTP6PmYdz+f9XVakFBv9gwK/6JuFalkkIl/87jv582L4MWDkLyNcGlTRaYAQQ01oLQ3IfW/04uqX2cztKcQ6QuVs7/oJjtd9zcAzg4wdP5WWQJgKqIS8vDxEREcjNzUV4uHs3x9/+3q9YtScTt16aiqev6eTszXEt8iEr/+Ezdmq3zF1Axg7tg9dRvLwBbz/rW2KM5GRwzqBltkxutQOOnPB8LPhbSIKeMQzJzRSO0oGCTCA/XQuE8uGpPkhjzD5QzZ/Haa/xC9TWKx9FJ/8Ati0FdizT1mkkJ9/ON2phKK5dw7dVWppknenbgJPbtPvsg/JmF/5e2WfhyUB4knaTlhJ18jWeiM1OyqbnQVqYVPfB1p+gJQiuew7Y+tHZgCth8C+PaS0HlirMBn79N7DpLa0lTgREAB1HaYFQ3k9u0sp1IRLSZD80KNAZlwVox3idblazW0N+J3W2pVY4kv0ux1Tn0UCn623bgnh8M/DtDODoRrNjcjTg5WPZeuTzJfuAFkBzjp7/55YQqsJRayAq1SyUNtN+TmuCaGW59v4Z8rm3A8iQz72dQN6xcwe0ev//mj2Pbattk5udvxmAPDgAHTpVgMEvrlOPV/9tEFrGhUKX5K/I04fOBh0VdnZqrQD18fbVPnDkBG08kdV3gjP+JWr+4StdOsW1WmjMW2/MW3AqSuqGodotROdsRTJbLs+NIcKTfmeH1wHbPgF2fwGUyQmyWpOuWhiSE1x4E22ZfIzlHj0bcoz30qpTn6BoIELCTdOzAccUdqrvHfnX97lk7QfW/ENrETMeI91uAgY92rATjoQ9ae2RIGXsmpOT6KV3A91v1QJw7Rai3GNnA5HcZL+qx0eB/JPWBZWGkv97pnAeAXj71G1xbWgXowTWXncAPW/TTtbWkkC/6ilg64fac/n/P+BBoM9U7f98Y8jPdvqwFoYklGRVByP5vcvnxPnIZ07tVjr1PFV7HhKv/REhn3Wmz75dQNZerVW3PvL98gdRUXUXdYNbl72AYc9ox5WTu/MYgBrJUwLQrP/uwKL1RzCkfTzemdgLulFZAZzYAhxeCxz+ATj667k/NKVeJaFj9a0TkNBB+2tG/mq1N2O3l/xlbN4dRjXJSW/fN1oYOrASqKo4Gwaa99c+fCXsSKisj9RbNOkCJHapvu8KhMbBraRvB1Y/o+0HIa0rPSYBA/6m1V7VlrYR+OUVrSvNGFgkOPa9D+hwjWUtfuakSFu6COXYbWhRt/Hr0pUixfMSbqTQ3RR0zB5L69KFTqDmReamLmljF3WRdiz8+s7ZLippfZKWQzk5y//vhiovATa8Dvz44tku0S5jgaGztYBsb9JqZwxGcpMAagykKohegLRMGSrr/5qEnISOQHyHs5998Rdpf1iZ72f5P1V0rho+eX5KawGWUCV6TQaufNb648sGGIAayRMCUG5ROS6duwrF5ZX46I7e6Nu6EX8BuTo5hKX76tA6LfAc+VmrU6ndVC7/wU1hR/7zd9Sab8l9yElh1wotDEkBb+3Wg7iLzMJOVyCxU91WDncmYX71U9pxbmwF6P1/WrCRrgoJPL+8ChzbdPZ72gwD+t6rhUW9FNtKN8+u/2qtXyd+P7u85WXApfcArYee+48N+TzZ8yXw3RNnW4lldOLw54CmPeESJFiat9SZWumMLXUntNYb+SNBWrNV0OlU/dnXQavl8rbRH1uyv+SYWzlTey41a9e/AwQ4p8eBAaiRPCEALVh3EM9+swftE8Pwzf0DPG/uH2k2lpOAdJPIvfwlYk5OBjKKRj7w5F7qJqQ5nTyHnJz2fqN1RUrgkYDriJY7V3BoLbDqaeD4b9pzaUUJjj57wpYWoi5jtG6a+PbQLTm9Hd0EbJivdaUau3SkyPjSO4Gu47Tjx0i6ib6dfjZghjUBhj6p1fq4U+ustLhJ95d0/TW2m66hdn4GrPg/rWtf/j/etPRsF7UDMQDpPADJr7T/c2twPKcYz9/QBTf2TIFH/EW3/zvthCehR/7Sqd3Ck9oHaDEIaDlI+w/IwEOeTD66ZSi7BCGp8xBSDyZ1L5dMqb9rTM/kM2Pjm8Dvi862EMv+6jER6HyDNqx987taSJJus373Af0ecFpLhtu2UC4eq3WPSX3dzUu1VicHYgDSeQDacTwXV7/6kxr6vmXW5Qj0c+MgcGofsOV9beiu+QgV6e6QZmkJOy0GAk176eevf6L65vSRk/pFI2u2aFBdMuJMisI3vAGc0eZIq0FqpC5/Sht1Rda1zn84WqtfkrrGG/8DtB4CVzx/cx4gD7R2rxYU+rWOdc/wIx9QO1cAv79fs5ZBRmV1ugFoNVhr7fGk2g4ia0nXTPsRzt4K9yGfG1I3JS1l0qosdUIy51JCZ2D4s9WF9WS16BbA7d8BS27R6jElDF39L6DHBLgaBiAPtHqPFoAGt4+H25CGyLQNwJYPtPAjozqMIxnaXAFcfAvQdphHT8pFRA4kXeTthmu3glPavDruVOfjyoKjgVtXAP+dCmxfCnxxn1afNnimS+1jBiAPc7qwDFuOavNHXNbODYb6yhwbfyzWgo8M9TSSkQsSeqRIUS63QERkL+42LYI78A0ArnsLiGoO/PA88NM8LQRd84bLzFvGAORh1u3LVI0pMvorKdJB1f+W1CpIHY8UI8rEhNLSs3/l2bkqZIKxjtdqwafZpfoZsktE5Im8vIDBj2shSFqBZEJPmUdq7EcuMQUJA5CHWbPnlPO6v2T2XmnRMc1LceTsvBTGZfXNQCrXebr4VqDjNazrISLyNBffrM28vmS8Nn/XO0OBm5cBMa2culkMQB6korIK6/Y5OADJtbS+n6NdPFSumF376t61ycRccqkBmXI9pRfQ7RYgrq1jtpWIiJyj5WVacbQURUsPwL+Hai1BMqDFSRiAPIjU/uQWlyMiyA/dUsymNLeXI79ocz6YXzxUhqdLwDFesK/2VaXlayxkJiLSn/j2wB3fa+cNmaH717cZgMi2o78GtY2Dr4+3/Wf9XD5Fu86PdGENma3NmyEzp3ICQiIiqo9M0DnxK+CHfwIDH4YzMQB5kDWOGv4us6l+86h2kcX2VwPX/9tx060TEZF78w/WLirrZAxAHuJETjH2pOfD20trAbLbKK5VTwI/v6Q973k7MOKfbPEhIiK3wwDkIdZUz/58cbMoRIX42+fiep9PBbYt0Z7LhFYD/sah6kRE5JYYgDys++sv9pj8sCQPWHqrdgVqmZn5r69qwxqJiIjcFAOQBygpr8TPB7LV47/Yuv5H5vX58AYgfbs2UeGNi4A2Q237HkRERA7GAOQBNh4+jeLySiSGB6JDExtevT5rP/DBddokhnIh0puWAsndbbd+IiIiJ2EA8qTur/Zx8LJVTc7RTcBHNwLFZ4DolsAtn2r3REREHoAByM0ZDAbT/D+XtbNR99eer4Flk4CKEiCpu9byw4sFEhGRB3H6dennz5+P5s2bIzAwEL1798amTZvO+dry8nI89dRTaNWqlXp9165d8e233zZqne7u4KlCpJ0ugr+PN/q3jm38Cn9bCCy5WQs/ba4AJn7J8ENERB7HqQFoyZIlmDZtGmbPno3ff/9dBZphw4YhM1Nr0ajtiSeewJtvvolXX30Vu3btwp133olrr70WW7ZssXqdntL91btlNEICGtmg9+s7wJcPAoYq7YrsYxcD/iG22VAiIiIX4mWQPhQnkdaZXr164bXXXlPPq6qqkJKSgnvvvRfTp0+v8/qkpCQ8/vjjuOeee0zLrr/+egQFBeGDDz6wap31ycvLQ0REBHJzcxEebsOiYju46e0N+OVgNmZd3QG39W/RuEkOX+oM5B0D+t0PDH2Sc/wQEZFbseT87bQWoLKyMmzevBlDh54dUu3t7a2er1+/vt7vKS0tVd1a5iT8/PTTT1av07he2WnmN3eQX1KOTYdP2+byF3/+oIWfwAjgsscYfoiIyKM5LQBlZWWhsrISCQkJNZbL8/T09Hq/R7qy5s2bh/3796uWnZUrV2L58uU4efKk1esUc+fOVYnReJMWI3fw0/4sVFQZ0CI2BM1jG9lV9cfH2n3H6wC/miGTiIjI0zi9CNoSL7/8Mtq0aYP27dvD398fU6dOxaRJk1QrT2PMmDFDNZcZb0ePHoU7MI7++ktjR3+VFgC7Ptced7vJBltGRETk2pwWgGJjY+Hj44OMjIway+V5YmJivd8TFxeHzz77DIWFhThy5Aj27NmD0NBQtGzZ0up1ioCAANVXaH5zdVVVBqzdd8o23V+7PwfKC4HoVkDTXrbZQCIiIhfmtAAkLTg9evTAqlWrTMukW0ue9+nT57zfK3VAycnJqKiowKeffopRo0Y1ep3uZueJPJzKL0WIvw96tYhq3Mr+WKzddx3H2h8iItIFp06EKMPVJ0yYgJ49e+KSSy7BSy+9pFp3pFtLjB8/XgUdqdERGzduxPHjx9GtWzd1P2fOHBVwHnnkkQav01MYu7/6tY5FgK+P9SvKOQoc/lF73HWMjbaOiIjItTk1AI0ZMwanTp3CrFmzVJGyBBuZ2NBYxJyWllajvqekpETNBXTo0CHV9TVixAi8//77iIyMbPA6PcXqvZm26f7aJsXPBqD5ACCymW02joiIyMU5dR4gV+Xq8wBlFZSi1zPfQ35zGx8bgoRwK0dtyQpe6wlkHwCueYMF0ERE5NbcYh4gst66vadUdumYFG59+BHHftPCj18wcNFIW24iERGRS2MA0nP31x8fafcX/RUICLPBlhEREbkHBiA3U15ZhR+qh7836urv5SXAjk+1x93G2WjriIiI3AMDkJvZfOQM8ksqEB3ij24pZ4u/LbbvW6AkFwhP1gqgiYiIdIQByM2sqe7+GtQ2Dj7eXo2f+6fLGMC7EcPoiYiI3BADkJtZY7z8RWPqfwoygf0rz05+SEREpDMMQG7k2Jki7MsogDT8DGwTa/2Kti8DDJVAcg8grq0tN5GIiMgtMAC5YetPj9QoRAb7N370F1t/iIhIpxiA3MhqW3R/pe8A0rcD3n5Ap+ttt3FERERuhAHITZSUV+KXg9mNn//HWPzc7kogONpGW0dEROReGIDcxPqD2SitqEJSRCDaJVg5aWFlBbBtqfa4Ky97QURE+sUA5GbdX5e1j4eXl5XD3w+uBgozgeBYoM3ltt1AIiIiN8IA5AbkerXGADS4nQ26vzqPBnz8bLR1RERE7ocByA0cyCzA8Zxi+Pt6o2/rGOtWUpwD7PlKe9x1rE23j4iIyN0wALkBY+tPn5YxCPb3tW4lO1cAlaVAfAegSVfbbiAREZGbYQByp+Hv7eKsX8kfH5+d+8faGiIiIiIPwQDk4vJKyvHbkTPq8eD2CdatJPsgcHQD4OUNdLnRthtIRETkhhiAXNzuE3morDKgaVQQmsUEN671p9VgICzRpttHRETkjhiAXFxGfqm6T44Msm4FVVU1u7+IiIiIAcjVZeaVqPuE8EDrVpD2C5CbBgSEA+2vsu3GERERuSkGIBeXYQpAAdatYGv13D8drwH8rGxFIiIi8jAMQC4uI6/U+hagskJg12faY176goiIyIQByE1agOKtCUAy8WFZARDVHGh2qe03joiIyE0xALm4zOoi6IQwK7rAtn6k3XPuHyIiohoYgFz8GmBna4AsbAHKPQ4cWqs95qUviIiIamAAcmEFpRUoKqtUj+MtLYLevlQiFJDaT+sCIyIiIhMGIDcogA4L9LX8GmDbJACx9YeIiKg+DECeOAdQeQmQuVt73Ha4HbaMiIjIvTEAubCMfCvnAMo9qnV/+YcBIbH22TgiIiK9BaA1a9bYfkvo3HMAhVnYAnTmT+0+KpWjv4iIiGwVgK688kq0atUKf//733H0qLQ2kEvNAWQKQCx+JiIislkAOn78OKZOnYply5ahZcuWGDZsGJYuXYqysjJrVkfnkGmaBTrAugAUmWqHrSIiItJpAIqNjcWDDz6IrVu3YuPGjWjbti3uvvtuJCUl4b777sMff/xh+y3VIavnAMo5ot2zBYiIiMg+RdDdu3fHjBkzVItQQUEBFi5ciB49emDAgAHYuXNnY1eva1YXQZvXABEREZHtAlB5ebnqAhsxYgRSU1Pxv//9D6+99hoyMjJw4MABtWz06NHWrl73tFmgtS6weEuKoA0G4AxbgIiIiM7Hwtn1NPfeey8WL16sTtK33nornn/+eXTq1Mn09ZCQELzwwguqS4ysk1tcjrKKKstngS4+A5TmaY8jm9lp64iIiHQYgHbt2oVXX30V1113HQICAs5ZJ8Th8tYztv5EBfshwNfH8vqf0ETAL8hOW0dERKTDALRq1aoLr9jXF4MGDbJm9dSYAmgOgSciIrJPDdDcuXNVsXNtsuy5556zZpVk8zmAWABNRERk0wD05ptvon379nWWd+zYEQsWLLBmlVRLZr5xFmhLR4CxAJqIiMguASg9PR1NmjSpszwuLg4nT560xXbpXqO7wDgJIhERkW0DUEpKCn7++ec6y2UZR37ZOgBZ2ALESRCJiIjsUwQ9efJkPPDAA2ouoMGDB5sKox955BH87W9/s2aVVItpDiBLWoCqKoGc6muzsQaIiIjItgHo4YcfRnZ2trr8hfH6X4GBgXj00UfVrNDUeJnWdIHlnQCqygEffyCsbhclERERNSIAeXl5qdFeM2fOxO7duxEUFIQ2bdqcc04gskxVleFsEbQlXWDG+p+IFMDbgrmDiIiIdMaqAGQUGhqKXr162W5rSDldVIaKKgO8vIDYUAsCEOt/iIiI7BuAfvvtNyxduhRpaWmmbjCj5cuXW7taMiuAjgkJgJ+PBXXqnAOIiIjIfqPAPv74Y/Tt21d1f61YsUIVQ8uV31evXo2IiAhrVklmMqsLoC2/CjxbgIiIiOwWgP7xj3/gX//6F7744gv4+/vj5Zdfxp49e3DjjTeiWTNegLOxeBkMIiIiFwxABw8exFVXXaUeSwAqLCxUhdEPPvgg3nrrLVtvo+6kWzsHECdBJCIisl8AioqKQn5+vnqcnJyMHTt2qMc5OTkoKiqyZpVU3xxAYRa0AJUVAYWZ2mO2ABEREdm+CHrgwIFYuXIlOnfujNGjR+P+++9X9T+ybMiQIdaskho7B5BxBFhgBBAUaactIyIi0nEAeu2111BSop2kH3/8cfj5+eGXX37B9ddfjyeeeMLW26g7GflWdIGxAJqIiMh+AaiiogJffvklhg0bpp57e3tj+vTplq6GGtAFZlELEOt/iIiI7FcD5OvrizvvvNPUAkS2VVFZhawC43XAOAkiERGRyxRBX3LJJdi6davtt4aQVVAGgwHw8fZCbIgVl8HgJIhERET2CUByEdRp06apWqD169dj27ZtNW6WmD9/Ppo3b64uptq7d29s2rTpvK9/6aWX0K5dO3X9sZSUFDX03rw1as6cOWpIvvmtffv2cLc5gOLDAuDt7dXwb2QNEBERkX2LoMeOHavu77vvPtMyCRoGg1y/yguVlZUNWs+SJUtUkFqwYIEKPxJupLZo7969iI+Pr/P6jz76SNUbLVy4UM1EvW/fPkycOFG957x580yv69ixI77//vuzP6Rvoy555pwAZEn9jzQZmWqAGICIiIguxKpkcPjwYdiChJbJkydj0qRJ6rkEoa+++koFnPoKq2WkWb9+/XDTTTep59JyNG7cOGzcuLHG6yTwJCYmwh1lGK8CH2ZB91dRNlBeKDEUiEyx38YRERHpOQClpja+zkQuoLp582bMmDHDtExGlA0dOlR1q9VHWn0++OAD1U0mdUiHDh3C119/jVtvvbXG6/bv34+kpCTVrdanTx/MnTv3vJfoKC0tVTejvLw8uNUcQMbWn/BkwNfC2aOJiIh0yKoAtGjRovN+ffz48RdcR1ZWluoqS0hIqLFcnst1xeojLT/yff3791fdbTIkX0akPfbYY6bXSFfae++9p+qETp48iSeffBIDBgxQs1WHhYXVu14JSPI617oOGAugiYiIXCoAyczP5uRq8HIJDLkuWHBwcIMCkDXWrl2rLsT6+uuvq6Bz4MABtS1PP/00Zs6cqV4zfPhw0+u7dOmiXictVkuXLsXtt99e73qlFUpqkcxbgKTA2qmXwbCmBYgF0ERERPYLQGfOnKmzTLqd7rrrLjz88MMNWkdsbCx8fHyQkZFRY7k8P1f9joQc6e6644471HO5FIdciHXKlClqRmrpQqstMjISbdu2VWHpXAICAtTNba8Ez0kQiYiI7D8Mvj5t2rTBs88+W6d16FyktahHjx5YtWqVaVlVVZV6LnU79ZFWptohR0KUkC6x+hQUFKir1zdp0gTuINNYBM1JEImIiOzGpuPDZfTViRMnGvx66XaaMGECevbsqYqaZRi8tOgYR4VJV5pcbV5qdMTIkSPVyLGLL77Y1AUmrUKy3BiEHnroIfVcur1kW2bPnq2+JqPFXF1pRSVOF5apxwmWXAmeNUBERET2D0Cff/55jefS+iIFxzIxogxTb6gxY8bg1KlTmDVrFtLT09GtWzd8++23psLotLS0Gi0+cqFVmfNH7o8fP464uDgVdp555hnTa44dO6bCTnZ2tvq6FExv2LBBPXZ1p6pbf/x9vBEZ7Newb6osB3KPa4/ZAkRERNQgXoZz9R2dR+1uKAklEjAGDx6MF1980W26m85FiqAjIiKQm5uL8PBwh73v5iNncP0bv6BpVBB+enRww77p9GHglW6AbyDweLr8Muy9mURERG5//raqBUhqdchF5gAy1v9IATTDDxERkWOLoKnxOAcQERGRCweg66+/Hs8991yd5c8//zxGjx5ti+3SJeNlMOKtKoBm/Q8REZFdA9APP/yAESNG1FkukxDK18iRcwBxCDwREZFDApDMrSPz+NTm5+fn1OtoubvM6lmgreoC4ySIRERE9g1AMgPzkiVL6iz/+OOP0aFDB2tWSda2AHESRCIiIotZNQpMJh+87rrr1AzLMvRdyAzOixcvxieffGLNKsmaIujSfKAoW3vMImgiIiL7BiCZfPCzzz5TFyZdtmwZgoKC1IVHv//+ewwaNMiaVepecVkl8koqLLsQqrH+JzgGCKj/SvdERERkw0thXHXVVepGtpGZr7X+BPn5ICyggb8W1v8QERE5rgbo119/xcaNG+ssl2W//fabdVuicxlmBdAys3aDsP6HiIjIcQHonnvuwdGjR+ssl+tzydfI+vqfBnd/CU6CSERE5LgAtGvXLnTv3r3OcrlKu3yNLMc5gIiIiFw8AAUEBCAjI6POcrkivK+v1WVFupZZPQt0QhjnACIiInLJAHTFFVdgxowZ6mqrRjk5OXjsscdw+eWX23L7dMPiFiCDgTVAREREVrKqueaFF17AwIEDkZqaqrq9xNatW5GQkID333/f2m3RtbM1QA1sASrIACpKAC8fIKKpfTeOiIjIw1gVgJKTk7Ft2zZ8+OGH+OOPP9Q8QJMmTcK4cePU5TCoMaPAAi3r/opIBny4z4mIiCxhdcFOSEgI+vfvj2bNmqGsrEwt++abb9T9X//6V2tXq0sGg8HyLjAWQBMRETk2AB06dAjXXnsttm/fruaskRO4+dw1lZWV1m+RDhWUVqCoTNtn8Q0tgmYBNBERkWOLoO+//360aNECmZmZCA4Oxo4dO7Bu3Tr07NkTa9eutX5rdN79JTNAhzR0FmgWQBMRETm2BWj9+vVYvXo1YmNj4e3tDR8fH9UdNnfuXNx3333YsmWL9VukQ5mWFkDXmASRAYiIiMghLUDSxRUWpl18U0LQiRMn1GMZFbZ3715rVqlrGdXXAeMkiERERC7cAtSpUyc1+ku6wXr37o3nn38e/v7+eOutt9CyZUvbb6WHs3gEWEUpkHdce8waICIiIscEoCeeeAKFhYXq8VNPPYWrr74aAwYMQExMDJYsWWLNKnXN4jmAcuQ6bAbALwQIibXvxhEREXkgqwLQsGHDTI9bt26NPXv24PTp04iKimr4lczJJNPYAhTWwBagHLP6H+5vIiIii9nswl3R0dG2WpXuWD4HEK8CT0RE5PAiaLJXEXRD5wBiATQREVFjMAC5xCzQVl4GgwXQREREVmEAcrLc4nKUVVRZWATNFiAiIqLGYAByMmPrT1SwHwJ8fRr2TawBIiIiahQGIHcrgC4+A5Tkao/ZBUZERGQVBiCXmQPIwqvAh8QD/sF23DIiIiLPxQDkZJn5xjmAWP9DRETkKAxATsY5gIiIiByPAchlAlBD5wDiVeCJiIgaiwHIRUaBWVwDxABERERkNQYgJ8u0tguMI8CIiIisxgDkRFVVhrNF0A3pAquqBHLlSvBsASIiImoMBiAnOl1Uhooqg7qge2xoAwJQ/kmgsgzw9gPCkxyxiURERB6JAcgFCqBjQgLg5+Pd8PqfyBTAu4GzRhMREVEdDEBOlGm6CKqFI8BY/0NERNQoDEDuNAcQJ0EkIiKyCQYgFxgCb/kcQGwBIiIiagwGICfKyK++DlgY5wAiIiJyJAYgJ+IcQERERM7BAOQuXWDlxUBBuvaYLUBERESNwgDkLkXQOWnafUAEEBRl5y0jIiLybAxATlJRWYWsAuN1wAIsKIBuBjVzIhEREVmNAchJsgvLUGUAfLy91ESIF8QCaCIiIpthAHJy91dcaIAKQRfEAmgiIiKbYQBykvTcEsvmAOIkiERERDbDAOQkGdVXgY+3dAg8AxAREVGjMQA5fQ6gBrQAGQysASIiIrIhBiBnD4FvyCzQRaeBsnztcUSKnbeMiIjI8zEAOX0SxMCGd3+FJQF+DewyIyIionNiAHJyC1CD5gDK4UVQiYiIbIkByEky861oAWL9DxERkWcEoPnz56N58+YIDAxE7969sWnTpvO+/qWXXkK7du0QFBSElJQUPPjggygpKWnUOh2ttKISpwvLLAhALIAmIiLymAC0ZMkSTJs2DbNnz8bvv/+Orl27YtiwYcjMzKz39R999BGmT5+uXr9792688847ah2PPfaY1et0hlPVrT9+Pl6ICva78DdwEkQiIiLPCUDz5s3D5MmTMWnSJHTo0AELFixAcHAwFi5cWO/rf/nlF/Tr1w833XSTauG54oorMG7cuBotPJau05kF0PFhgfBqyHW9OAkiERGRZwSgsrIybN68GUOHDj27Md7e6vn69evr/Z6+ffuq7zEGnkOHDuHrr7/GiBEjrF6nKC0tRV5eXo2by8wBVFkB5BzVHrMImoiIyCZ84SRZWVmorKxEQkJCjeXyfM+ePfV+j7T8yPf1798fBoMBFRUVuPPOO01dYNasU8ydOxdPPvkkHD4HUEPqf/KOA4ZKwCcACE20/8YRERHpgNOLoC2xdu1a/OMf/8Drr7+u6nuWL1+Or776Ck8//XSj1jtjxgzk5uaabkePVre42PkyGA0rgD6s3Uc2k+Ysu24XERGRXjitBSg2NhY+Pj7IyMiosVyeJybW39Ixc+ZM3HrrrbjjjjvU886dO6OwsBBTpkzB448/btU6RUBAgLq55BxAxgLo6BZ23ioiIiL9cFqTgr+/P3r06IFVq1aZllVVVannffr0qfd7ioqKVE2POQk8QrrErFmnM2QaZ4FuyGUwTle3AEUxABEREbl9C5CQ4eoTJkxAz549cckll6g5fqRFR0ZwifHjxyM5OVnV6IiRI0eqUV4XX3yxmt/nwIEDqlVIlhuD0IXW6QqMLUCJERZ0gbEFiIiIyDMC0JgxY3Dq1CnMmjUL6enp6NatG7799ltTEXNaWlqNFp8nnnhCDRuX++PHjyMuLk6Fn2eeeabB63QFZ4ugAyxoAeIQeCIiIlvxMkjfEdUgw+AjIiJUQXR4eLhN111cVomLZn2rHm+bcwXCA88zEaL8ap5tBpTmAXdvBOLb23RbiIiI9Hr+5rAiB8vM11p/gvx8EBZwgQa44jNa+BGcA4iIiMhmGICcNAu0dH9dcBZoY/dXWBLgF+SArSMiItIHBiAHOzsE3oICaNb/EBER2RQDkINZNAs0R4ARERHZBQOQg2UaZ4EOa8gIsOpJEDkHEBERkU0xALlDCxC7wIiIiGyKAcjBeBkMIiIi52MActZlMC7UAlReAuSd0B6zC4yIiMimGIBctQss54jMhAgEhAPB0Y7ZOCIiIp1gAHKggtIKFJZVqsfxFyqCNl0CIxW40HxBREREZBEGICe0/sgM0CEXmgXaWP/D7i8iIiKbYwBy2QJozgFERERkLwxArlgAXaMLjAGIiIjI1hiAHIhzABEREbkGBiAHSowIxIA2seiUHHH+F1ZVAWdkFBi7wIiIiOzhApW4ZEujuiWr2wXlnwQqSwFvXyC8qSM2jYiISFfYAuSKjN1fkc0AH2ZUIiIiW2MAckWmAmjW/xAREdkDA5Ar4hxAREREdsUA5Io4BxAREZFdMQC5Is4BREREZFcMQK6IcwARERHZFQOQqynOAYrPaI8ZgIiIiOyCAchVC6BD4oGAUGdvDRERkUdiAHI17P4iIiKyOwYgVy2A5ggwIiIiu2EAcjWcA4iIiMjuGIBcDecAIiIisjsGIFdz2tgCxBogIiIie2EAciUVZUDeMe0xu8CIiIjshgHIleQeBQxVgF8IEBrv7K0hIiLyWAxArnoVeC8vZ28NERGRx2IAciWcA4iIiMghGIBccQg8R4ARERHZFQOQq3aBERERkd0wALkSzgFERETkEAxArsJg4CzQREREDsIA5CoKMoHyIsDLG4hIcfbWEBEReTQGIFfr/opoCvj6O3triIiIPBoDkKtgATQREZHDMAC53BxArP8hIiKyNwYgV8E5gIiIiByGAcjlusAYgIiIiOyNAchV8DIYREREDsMA5ApK84HCU9pjdoERERHZHQOQKzhzRLsPigYCI5y9NURERB6PAcgV8BIYREREDsUA5Ao4BxAREZFDMQC5As4BRERE5FAMQK6AcwARERE5FAOQK+AcQERERA7FAORslRVA7lHtMWuAiIiIHIIByNnyjgFVFYBPABDWxNlbQ0REpAsMQK40Asybvw4iIiJH4BnX2TgHEBERkcMxADkb5wAiIiLSZwCaP38+mjdvjsDAQPTu3RubNm0652svu+wyeHl51bldddVVptdMnDixztevvPJKuPQQeI4AIyIichhfONmSJUswbdo0LFiwQIWfl156CcOGDcPevXsRHx9f5/XLly9HWVmZ6Xl2dja6du2K0aNH13idBJ53333X9DwgIAAuiV1gRERE+msBmjdvHiZPnoxJkyahQ4cOKggFBwdj4cKF9b4+OjoaiYmJptvKlSvV62sHIAk85q+LioqCyzEYgNPGFiB2gREREekiAElLzubNmzF06NCzG+TtrZ6vX7++Qet45513MHbsWISEhNRYvnbtWtWC1K5dO9x1112qpehcSktLkZeXV+PmEEXZQFk+AC8gMtUx70lERETODUBZWVmorKxEQkJCjeXyPD09/YLfL7VCO3bswB133FGn+2vRokVYtWoVnnvuOaxbtw7Dhw9X71WfuXPnIiIiwnRLSUmBQ+t/wpMAv0DHvCcRERE5vwaoMaT1p3PnzrjkkktqLJcWISP5epcuXdCqVSvVKjRkyJA665kxY4aqQzKSFiCHhCBeAoOIiEh/LUCxsbHw8fFBRkZGjeXyXOp2zqewsBAff/wxbr/99gu+T8uWLdV7HThwoN6vS71QeHh4jZtjrwLP+h8iIiLdBCB/f3/06NFDdVUZVVVVqed9+vQ57/d+8sknqnbnlltuueD7HDt2TNUANWniYpeaMLYARTMAERER6WoUmHQ9vf322/jPf/6D3bt3q4Jlad2RUWFi/Pjxqouqvu6va665BjExMTWWFxQU4OGHH8aGDRvw559/qjA1atQotG7dWg2vdymcA4iIiEifNUBjxozBqVOnMGvWLFX43K1bN3z77bemwui0tDQ1MsyczBH0008/4bvvvquzPulS27ZtmwpUOTk5SEpKwhVXXIGnn37a9eYC4hxARERETuFlMMhkNGROiqBlNFhubq796oHKi4FnquucHjkMBEfb532IiIh0Is+C87fTu8B0y9j9FRABBLngJI1EREQejAHI2QFICqC9vJy9NURERLrCAOQsnAOIiIjIaRiAnIVzABERETkNA5DTu8DYAkRERORoDEDOwi4wIiIip2EAcoaqSiDniPaYLUBEREQOxwDkDHkngMoywNsPCE929tYQERHpDgOQM+t/IpsB3j7O3hoiIiLdYQByBl4Cg4iIyKkYgJxaAM0h8ERERM7AAOTUOYDYAkREROQMDEDOwDmAiIiInIoByBk4BxAREZFTMQA5WvEZoCRHexyV6uytISIi0iUGIGe1/oQmAP4hzt4aIiIiXWIAclb9D7u/iIiInIYByNE4BxAREZHTMQA5GucAIiIicjoGIEdjFxgREZHTMQA5GucAIiIicjoGIEeqKAVyj2mP2QJERETkNAxAjpSTBsAA+IUAIbHO3hoiIiLdYgByRgG0dH95eTl7a4iIiHSLAcgpBdAcAUZERORMDECOVF4I+AaxAJqIiMjJfJ29AbrS/0Gg3wNaMTQRERE5DVuAHE1qf/wCnb0VREREusYARERERLrDAERERES6wwBEREREusMARERERLrDAERERES6wwBEREREusMARERERLrDAERERES6wwBEREREusMARERERLrDAERERES6wwBEREREusMARERERLrj6+wNcEUGg0Hd5+XlOXtTiIiIqIGM523jefx8GIDqkZ+fr+5TUlKcvSlERERkxXk8IiLivK/xMjQkJulMVVUVTpw4gbCwMHh5edk8nUqwOnr0KMLDw6FX3A8a7oezuC803A8a7gcN94Nl+0IijYSfpKQkeHufv8qHLUD1kJ3WtGlTu76H/PL0fjAL7gcN98NZ3Bca7gcN94OG+6Hh++JCLT9GLIImIiIi3WEAIiIiIt1hAHKwgIAAzJ49W93rGfeDhvvhLO4LDfeDhvtBw/1gv33BImgiIiLSHbYAERERke4wABEREZHuMAARERGR7jAAERERke4wADnQ/Pnz0bx5cwQGBqJ3797YtGkT9GbOnDlqdm3zW/v27eHpfvjhB4wcOVLNTio/82effVbj6zIWYdasWWjSpAmCgoIwdOhQ7N+/H3rbDxMnTqxzfFx55ZXwNHPnzkWvXr3UbPPx8fG45pprsHfv3hqvKSkpwT333IOYmBiEhobi+uuvR0ZGBvS2Hy677LI6x8Sdd94JT/PGG2+gS5cupkn++vTpg2+++UZXx0ND9oMtjwcGIAdZsmQJpk2bpobw/f777+jatSuGDRuGzMxM6E3Hjh1x8uRJ0+2nn36CpyssLFS/cwnB9Xn++efxyiuvYMGCBdi4cSNCQkLU8SEfenraD0ICj/nxsXjxYniadevWqZPZhg0bsHLlSpSXl+OKK65Q+8fowQcfxBdffIFPPvlEvV4uz3PddddBb/tBTJ48ucYxIf9fPI1cfeDZZ5/F5s2b8dtvv2Hw4MEYNWoUdu7cqZvjoSH7wabHgwyDJ/u75JJLDPfcc4/peWVlpSEpKckwd+5cg57Mnj3b0LVrV4OeyX+7FStWmJ5XVVUZEhMTDf/85z9Ny3JycgwBAQGGxYsXG/SyH8SECRMMo0aNMuhNZmam2h/r1q0z/f79/PwMn3zyiek1u3fvVq9Zv369QS/7QQwaNMhw//33G/QoKirK8O9//1u3x0Pt/WDr44EtQA5QVlam0qx0a5hfb0yer1+/HnojXTvSBdKyZUvcfPPNSEtLg54dPnwY6enpNY4PuZaNdJPq8fhYu3at6g5p164d7rrrLmRnZ8PT5ebmqvvo6Gh1L58X0hpifkxIV3GzZs08+piovR+MPvzwQ8TGxqJTp06YMWMGioqK4MkqKyvx8ccfq5Yw6QLS6/FQWWs/2Pp44MVQHSArK0v9IhMSEmosl+d79uyBnshJ/b333lMnN2m6fPLJJzFgwADs2LFD1QHokYQfUd/xYfyaXkj3lzTrt2jRAgcPHsRjjz2G4cOHqw95Hx8feKKqqio88MAD6Nevn/pAF/J79/f3R2RkpG6Oifr2g7jpppuQmpqq/mjatm0bHn30UVUntHz5cnia7du3qxO9dH1Lnc+KFSvQoUMHbN26VVfHw/Zz7AdbHw8MQORQcjIzkkI3CURyMC9duhS33367U7eNnG/s2LGmx507d1bHSKtWrVSr0JAhQ+CJpAZG/gDQQy2cNfthypQpNY4JGSggx4IEZDk2PIn8YShhR1rCli1bhgkTJqh6H71pd479ICHIlscDu8AcQJrq5K/X2hX78jwxMRF6Jn/RtG3bFgcOHIBeGY8BHh91STep/P/x1ONj6tSp+PLLL7FmzRpV/Gkkv3fpOs/JydHFMXGu/VAf+aNJeOIxIa08rVu3Ro8ePdQIORkw8PLLL+vuePA/x36w9fHAAOSgX6b8IletWlWjuVeem/dr6lFBQYFK7pLi9Uq6e+RDzPz4yMvLU6PB9H58HDt2TNUAedrxITXgctKXpv3Vq1erY8CcfF74+fnVOCakmV/q5TzpmLjQfqiPtAwITzsm6iPnidLSUt0cDxfaDzY/HmxSSk0X9PHHH6tRPe+9955h165dhilTphgiIyMN6enpBj3529/+Zli7dq3h8OHDhp9//tkwdOhQQ2xsrBr94cny8/MNW7ZsUTf5bzdv3jz1+MiRI+rrzz77rDoe/vvf/xq2bdumRkK1aNHCUFxcbNDLfpCvPfTQQ2pUixwf33//vaF79+6GNm3aGEpKSgye5K677jJERESo/wsnT5403YqKikyvufPOOw3NmjUzrF692vDbb78Z+vTpo2562g8HDhwwPPXUU+rnl2NC/n+0bNnSMHDgQIOnmT59uhr9Jj+nfAbIcy8vL8N3332nm+PhQvvB1scDA5ADvfrqq+oA9vf3V8PiN2zYYNCbMWPGGJo0aaL2QXJysnouB7WnW7NmjTrh177JsG/jUPiZM2caEhISVFAeMmSIYe/evQY97Qc56V1xxRWGuLg4NeQ3NTXVMHnyZI/8I6G+fSC3d9991/QaCb933323GgIcHBxsuPbaa1U40NN+SEtLUye36Oho9f+idevWhocfftiQm5tr8DS33XabOubls1H+D8hngDH86OV4uNB+sPXx4CX/WN5uREREROS+WANEREREusMARERERLrDAERERES6wwBEREREusMARERERLrDAERERES6wwBEREREusMARETUAHJBVi8vrzrXYyIi98QARERERLrDAERERES6wwBERG5zRei5c+eqK4YHBQWha9euWLZsWY3uqa+++gpdunRBYGAgLr30UuzYsaPGOj799FN07NgRAQEBaN68OV588cUaX5crTj/66KNISUlRr2ndujXeeeedGq/ZvHkzevbsieDgYPTt21ddlZuI3A8DEBG5BQk/ixYtwoIFC7Bz5048+OCDuOWWW7Bu3TrTax5++GEVan799VfExcVh5MiRKC8vNwWXG2+8EWPHjsX27dsxZ84czJw5E++9957p+8ePH4/FixfjlVdewe7du/Hmm28iNDS0xnY8/vjj6j1+++03+Pr64rbbbnPgXiAiW+HFUInI5UnLTHR0NL7//nv06dPHtPyOO+5AUVERpkyZgr/85S/4+OOPMWbMGPW106dPo2nTpirgSPC5+eabcerUKXz33Xem73/kkUdUq5EEqn379qFdu3ZYuXIlhg4dWmcbpJVJ3kO2YciQIWrZ119/jauuugrFxcWq1YmI3AdbgIjI5R04cEAFncsvv1y1yBhv0iJ08OBB0+vMw5EEJgk00pIj5L5fv3411ivP9+/fj8rKSmzduhU+Pj4YNGjQebdFutiMmjRpou4zMzNt9rMSkWP4Ouh9iIisVlBQoO6ltSY5ObnG16RWxzwEWUvqihrCz8/P9Fjqjoz1SUTkXtgCREQur0OHDiropKWlqcJk85sULBtt2LDB9PjMmTOqW+uiiy5Sz+X+559/rrFeed62bVvV8tO5c2cVZMxriojIc7EFiIhcXlhYGB566CFV+CwhpX///sjNzVUBJjw8HKmpqep1Tz31FGJiYpCQkKCKlWNjY3HNNdeor/3tb39Dr1698PTTT6s6ofXr1+O1117D66+/rr4uo8ImTJigipqlCFpGmR05ckR1b0kNERF5FgYgInILElxkZJeMBjt06BAiIyPRvXt3PPbYY6YuqGeffRb333+/quvp1q0bvvjiC/j7+6uvyWuXLl2KWbNmqXVJ/Y4EpokTJ5re44033lDru/vuu5GdnY1mzZqp50TkeTgKjIjcnnGElnR7STAiIroQ1gARERGR7jAAERERke6wC4yIiIh0hy1AREREpDsMQERERKQ7DEBERESkOwxAREREpDsMQERERKQ7DEBERESkOwxAREREpDsMQERERKQ7DEBERESkO/8PuXvLs0K90OwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    \n",
    "    # preprocessing of the data, delete transient part\n",
    "    M_df_no_transient, M_pos, M_neg = eliminate_transient(M_df)\n",
    "    MV_df_no_transient, MV_pos, MV_neg = eliminate_transient(MV_df)\n",
    "    MF_df_no_transient, MF_pos, MF_neg = eliminate_transient(MF_df)\n",
    "\n",
    "    # normalisation of datasets\n",
    "    M_data, M_label, M_train_data, M_train_label, M_valid_data, M_valid_label, M_test_data, M_test_label = shuffle_normalise_split_dataset(M_df_no_transient, 0.6, 0.2, 0.2)\n",
    "    MV_data, MV_label, MV_train_data, MV_train_label, MV_valid_data, MV_valid_label, MV_test_data, MV_test_label = shuffle_normalise_split_dataset(MV_df_no_transient, 0.6, 0.2, 0.2)\n",
    "    MF_data, MF_label, MF_train_data, MF_train_label, MF_valid_data, MF_valid_label, MF_test_data, MF_test_label = shuffle_normalise_split_dataset(MF_df_no_transient, 0.6, 0.2, 0.2)\n",
    "    \n",
    "    # set the dataste for training, validation and testing\n",
    "    # the validation is to see if the data is overfitting, in this case, we can use another dataset to check\n",
    "    # the MF data is only for evaluation, therefore, we can use all of it\n",
    "    train_len = min([len(M_train_label),len(MV_train_label)])\n",
    "    train_set, train_label = M_train_data[0:train_len,:], M_train_label[0:train_len]\n",
    "    valid_set, valid_label = np.concatenate((MV_valid_data,MF_valid_data),axis=0), np.concatenate((MV_valid_label,MF_valid_label))\n",
    "    # test_set, test_label = MF_data, MF_label\n",
    "    # train the model\n",
    "    training_epoch = 35\n",
    "    fnn_basic_no_fft_model, train_val_history = fnn_training_no_fft(train_set, train_label, valid_set, valid_label, epoch = training_epoch)\n",
    "    # evaluation\n",
    "    log_filename = \"fnn_no_fft.txt\"\n",
    "    plot_the_train_history(train_val_history, filename = f\"Basic fnn no fft {i} epoch {training_epoch}\")\n",
    "    evaluation(fnn_basic_no_fft_model,M_test_data,M_test_label,\"Basic test dataset\",filename=log_filename, model_name=\"basic fnn no fft\")\n",
    "    evaluation(fnn_basic_no_fft_model,MV_test_data,MV_test_label,\"CDD-based test dataset\",filename=log_filename, model_name=\"basic fnn no fft\")\n",
    "    evaluation(fnn_basic_no_fft_model,MF_data,MF_label,\"External dataset\",filename=log_filename, model_name=\"basic fnn no fft\")\n",
    "\n",
    "    # repeat for MV dataset\n",
    "    train_set, train_label = MV_train_data[0:train_len,:], MV_train_label[0:train_len]\n",
    "    valid_set, valid_label = np.concatenate((M_valid_data,MF_valid_data),axis=0), np.concatenate((M_valid_label,MF_valid_label))\n",
    "    # test_set, test_label = MF_data, MF_label\n",
    "    # train the model\n",
    "    training_epoch = 35\n",
    "    fnn_CDD_no_fft_model, train_val_history = fnn_training_no_fft(train_set, train_label, valid_set, valid_label, epoch = training_epoch)\n",
    "    # evaluation\n",
    "\n",
    "    plot_the_train_history(train_val_history, filename = f\"CDD-based fnn no fft {i} epoch {training_epoch}\")\n",
    "    evaluation(fnn_CDD_no_fft_model,M_test_data,M_test_label,\"Basic test dataset\",filename=log_filename, model_name=\"CDD-based fnn no fft\")\n",
    "    evaluation(fnn_CDD_no_fft_model,MV_test_data,MV_test_label,\"CDD-based test dataset\",filename=log_filename, model_name=\"CDD-based fnn no fft\")\n",
    "    evaluation(fnn_CDD_no_fft_model,MF_data,MF_label,\"External dataset\",filename=log_filename, model_name=\"CDD-based fnn no fft\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marti\\.conda\\envs\\causality_3\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "# Save the models and output data to disk\n",
    "fnn_basic_no_fft_model.save(f'fnn_basic_no_fft_model_{i}.h5')\n",
    "fnn_CDD_no_fft_model.save(f'fnn_CDD_no_fft_model_{i}.h5')"
   ]
  }
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